social media platforms research paper

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Social media platforms: a primer for researchers

Affiliations.

1 Department of Internal Medicine No. 2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine.
2 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK.
PMID: 33976459
PMCID: PMC8103414
DOI: 10.5114/reum.2021.102707

Social media platforms play an increasingly important role in research, education, and clinical practice. As an inseparable part of open science, these platforms may increase the visibility of research outputs and facilitate scholarly networking. The editors who ethically moderate Twitter, Facebook, and other popular social media accounts for their journals may engage influential authors in the post-publication communication and expand societal implications of their publications. Several social media aggregators track and generate alternative metrics which can be used by researchers for visualizing trending articles in their fields. More and more publishers showcase their achievements by displaying such metrics along with traditional citations. The Scopus database also tracks both metrics to offer a comprehensive coverage of the indexed articles' impact. Understanding the advantages and limitations of various social media channels is essential for actively contributing to the post-publication communication, particularly in research-intensive fields such as rheumatology.

Keywords: periodicals as topic; publication ethics; rheumatology; social media.

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Conflict of interest statement

The authors declare no conflict of interest.

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Open Access

Peer-reviewed

Research Article

Social media usage to share information in communication journals: An analysis of social media activity and article citations

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Radio Television and Cinema, Selcuk University, Konya, Turkey

Yasemin Özkent

Published: February 9, 2022
https://doi.org/10.1371/journal.pone.0263725
Reader Comments

Social media has surrounded every area of life, and social media platforms have become indispensable for today’s communication. Many journals use social media actively to promote and disseminate new articles. Its use to share the articles contributes many benefits, such as reaching more people and spreading information faster. However, there is no consensus in the studies that to evaluate between tweeted and non-tweeted papers regarding their citation numbers. Therefore, it was aimed to show the effect of social media on the citations of articles in the top ten communication-based journals. For this purpose, this work evaluated original articles published in the top 10 communication journals in 2018. The top 10 communication-based journals were chosen based on SCImago Journal & Country Rank (cited in 2019). Afterward, it was recorded the traditional citation numbers (Google Scholar and Thompson-Reuters Web of Science) and social media exposure of the articles in January 2021 (nearly three years after the articles’ publication date). It was assumed that this period would allow the impact of the published articles (the citations and Twitter mentions) to be fully observed. Based on this assessment, a positive correlation between exposure to social media and article citations was observed in this study.

Citation: Özkent Y (2022) Social media usage to share information in communication journals: An analysis of social media activity and article citations. PLoS ONE 17(2): e0263725. https://doi.org/10.1371/journal.pone.0263725

Editor: Marcelo Hermes-Lima, Universidade de Brasilia, BRAZIL

Received: May 3, 2021; Accepted: January 25, 2022; Published: February 9, 2022

Copyright: © 2022 Yasemin Özkent. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are available from: https://github.com/yaseminozkent/minimal-data-set.git .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The social network has become a tool for bringing people together, allowing individuals to list the users they are connected to, and to see other users’ connections [ 1 ]. Social media platforms (blogs, social networking sites, microblogging, etc.) contain all Web 2.0-based services. Social media has surrounded every area of life, and social media platforms are indispensable for today’s communication [ 2 ]. Scientists from various fields frequently use SoMe, especially Twitter, in most of their professional activities [ 3 ].

Scientists often use social media platforms to produce and debate ideas, share real-time information, spread their research, and find collaborators [ 2 , 4 ]. The way information is collected, disseminated and consumed has been significantly changed because social media by it is encompassing and easily accessible. There has been a significant increase in the number of studies related to social media with an increase in the use of the Internet [ 5 ]. The increased use of social media has also significantly affected how research is spread. Circulated articles through social media are more visible than not circulated articles [ 6 ]. Various scientific studies have examined this relationship, and most have found a positive correlation between article citations and Twitter exposure [ 7 – 9 ].

Studies focusing on new media technologies in connection with the digital age since the 2000s have taken an important place in communication studies [ 10 ]. In their extensive studies on research topics in communication journals, Elisabeth Günther and Emese Domahidi (2017) observed that the Internet and social media have become the most important focus for communication research, in parallel with classical media, such as TV or newspaper [ 10 ]. Social media is an important field of study and practice in both interdisciplinary and communication fields [ 11 – 14 ]. Social media research is encouraged in the field of communication, as people today present themselves through digitally networked platforms. Therefore, this study aimed to demonstrate the effect of social media on citation numbers of articles in communication-based journals. The relationship between the traditional citation numbers of articles and social media posts was analyzed in present study. Thus, it was aimed to shed light on the relationship between social media usage and the number of article citations in the field of communication.

Literature review

The emergence of social media took place simultaneously with Web 2.0. With the introduction of Web 2.0 into our lives, the Internet has become individualized, and use of social networks increased gradually. The Internet has become an interactive virtual world from a read-only state, and it has brought a different dimension to communication [ 12 ]. Today, social media is a wide network of interactions where people from many areas [ 15 ]. In particular, Internet has become a part of life due to the widespread use of smartphones. The majority of people actively use social media in daily life [ 16 ]. A recent study has stated that 70% of peoples in the USA have at least one social media account nowadays. The peoples over 65 years old of 62% have a social media account and they are regularly on social media. This observation is also similar for adolescents. The using of social networks has been reported as 77% for teenagers aged 13–16 years in 25 European countries [ 16 , 17 ]. These social network users interact for an average of more than 2.4 hours a day on social media [ 18 ]. This increased instant interaction has further increased the use of social media. Beside, social media platforms allows independent sharing, regardless of age, venue, and gender. Thus, information spreads rapidly across a wide area [ 15 , 18 ]. Additionally, social media platforms ensure simple interaction pathways between people, companies, and scientists without leaving the desk. Therefore, many scientists use social media in their personal or professional lives [ 19 ].

The most commonly used platform for the purpose of spreading science is Twitter [ 20 ]. Twitter is the most popular microblogging platform nowadays. This platform allows the publication of short messages by its users and enables them to communicate with each other. Evan Williams, Biz Stone, and Jack Dorsey created Twitter in March 2006 and brought into use in July 2006. Twitter has become one of the 10 most visited websites in 2013 and was defined as “the SMS of the Internet” [ 21 , 22 ]. In 2019, it was reported that there were 330 million monthly and 145 million daily active Twitter users. Nowadays, it was reached 339 million users in 2020 [ 23 ]. The number of Twitter users has been increasing daily. Today, Twitter ranks as the world’s second most widely used social network. Twitter users can follow a conversation and discuss a topic using messages named “tweets.” Tweets are constrained to 140 characters of text. Later, this limit was increased to 280 characters. Twitter allows the sharing of photos or videos [ 24 ]. Twitter was initially used to share news about the lives of celebrities. Afterward, it reached a broader audience quickly, especially with the participation of famous names and the involvement of political campaigns. On average, approximately 98 thousand tweets are sent every minute on the platform, allowing an excellent interaction. Given the rise in popularity of Twitter, its use is increasing in all parts of society [ 25 ]. This widespread use has also caught the attention of the scientific community. The use of Twitter as a tool for the dissemination of academic articles has soon become the focus of attention in the scientific world.

Academic output has increased gradually worldwide. Therefore, eliminating the relevant from the irrelevant has become essential for the scientific world. Thus, an impact scale has been necessitated the published articles [ 26 ]. The primary impact of the published article has been measured by its citations [ 26 ]. However, article citations have recently become questionable due to negative factors, such as the slow process of identifying truly impactful articles. The long wait time required the emergence of the articles’ importance has led to need for an alternative metric scale [ 27 ].

Today, non-traditional metrics, altmetrics, are increasingly used to measure the real-time reach and influence of a scientific article [ 26 , 28 ]. The term altmetrics was first proposed by Jason Priem in 2010 [ 29 ]. Thereafter, it has gained wide use in highlighting previously unknown and unrecognized scholarly impact metrics of studies [ 26 , 30 ]. The alternative metric scores play a role in complementing traditional metrics or indicators [ 31 ]. Many publishers, such as SAGE, Taylor-Francis Group, Elsevier, Nature Publishing Group, and Public Library of Science provide much information to their readers by their altmetrics evaluating system [ 32 ]. These altmetrics are calculated by various methods, including “Altmetric” and “Plum Analytics” [ 33 ]. All altmetrics that are an alternative to the traditional citation system provide a score for the research output.

These scores have become attractive for researchers [ 34 ]. Many scientists believe that these alternative metric scores show the real impact of published articles [ 35 – 37 ]. Many web-based platforms play a significant role in obtaining an altmetric score [ 11 , 31 ]. With the data obtained from these platforms, a digital score was acquired for academic output [ 11 ]. All altmetrics are based on the using social media and other online tools for disseminating scholarly information [ 38 ]. The use of social media platforms contributes significantly the spread of shared information in a wider environment. Thus, the sharing of academic output on social media accounts can reach more people faster by eliminating the waiting period in the traditional citation system [ 39 , 40 ]. They have also allowed the impact of articles to be more immediately determined, contrary to traditional citation metrics [ 41 ].

However, some concerns remain that the altmetric score can be manipulated [ 42 , 43 ]. In particular, the use of automatic bot can affect the altmetric scores of articles [ 44 ]. Further, Twitter accounts can affect the results of shared articles. These account holders may be social workers, companies, or politicians, and would have more followers than others [ 45 , 46 ]. Thus, some researchers suggest the use of “alt‑index” to measure the social visibility of scientific research [ 6 ]. Similarly, Haustein et al. argued that social media metrics could not actually be regarded as alternatives to traditional citations; hence, they proposed these metrics as promoters of traditional citations [ 11 ]. The authors suggested the use of “Twitter Coupling” to deal with these concerns [ 38 ]. Although this may be a solution there is a consensus in many studies that social media usage will increase the impact of academic papers, thus ignoring these concerns [ 47 , 48 ].

The potential of social media platforms to connect with other fields raises various scientific questions [ 49 ]. Therefore, the papers in the social sciences and humanities are more often found on social media platforms [ 11 ]. Today, social interaction is so intertwined with media that it is not possible to separate social media from the media sector. Thus, most studies related to social media have been published in the communication sciences. This increased usage of these social networks has led to the research question, “How are altmetrics and citation measures related in communication journals?” Tonia et al. (2016) stated that there were no statistical differences between tweeted and non-tweeted papers regarding their citation numbers [ 50 ]. Costas et al. (2015) found only weak correlations in citations suggested by altmetrics and traditional citation analysis [ 35 ]. Further, some articles can be more attractive than other on social media platforms. Hence, some researchers have argued that there is a difference between social impact and real impact [ 11 ]. However, Thelwall et al. (2013) found that altmetrics was associated with citation counts [ 51 ]. Another study stated that there is a positive relationship between social media posts and academic citations [ 52 ]. Similarly, Shuai et al. (2012) detected significant correlations between tweets and early citations on 4,606 pre-prints articles [ 8 ]. Nevertheless, there is no consensus in the studies that to evaluate between tweeted and non-tweeted papers regarding their citation numbers in communication science, which is the scientific field mostly associated with social media and Twitter. The studies related to social media are encouraged in the field of communication as people present themselves through digitally networked platforms today. Therefore, it was hypothesized that there might be a correlation between Twitter posts and traditional citations the articles in the top ten communication-based journals.

Materials and methods

Study design.

The present study was designed as a retrospective cross-sectional study. The aim was to examine the effect of Twitter and other social media platforms on academic citations. Therefore, the top ten communication-based journals [ 9 ] were evaluated based on the SCImago Journal & Country Rank (cited in 2019). It was used the SCImago Journal & Country Rank search field to select journals and filtered only “communication” journals. The selection criteria were the top ten communication-based journals according to their SCImago Journal Rank indicator. This indicator is a measure of journals’ academic impact and accounts for both the number of citations received by a journal and the importance or prestige of the journals in which the citations [ 53 ]. The impact factor of these journals was ≥ 2, and the quartile (Q) index was Q1. All ten journals had similar indexes and similar impact factors. It was assumed that this would reduce the potential for unwanted variation differences in social media activity.

Impact Factor: The impact factor has been defined as an indicator of academic journal. It reflects the year’s average number of citations per paper published during the preceding two years. It is often used as a relative indicator of a journal’s importance in its field. The journals with high impact factors are thought to be more important than those with low impact factors [ 54 ].

Q index: The journals’ rank in each specific category is separated into quartiles by the Journal Citation Report and SCImago Journal and Country Rank: Q1, Q2, Q3 and Q4. Q1 comprises the top 25% of journals in the list; Q2, Q3, and Q4 comprise 25% to 50%, 50% to 75%, and 75% to 100% of journals in the list, respectively ( https://www.mondragon.edu/en/web/biblioteka/publications-impact-indexes ).

These Journals “Political Communication (Q1, IF: 4.339)”, “Journal of Advertising (Q1, IF: 6.302)”, “Journal of Communication (Q1, IF: 4.846)”, Big Data and Society (Q1, IF: 4.577), Applied Linguistics (Q1, IF: 4.286), Communication Methods and Measures (Q1, IF: 5.281), New Media and Society (Q1, IF: 4.577), Human Communication Research (Q1, IF: 3.540), Public Opinion Quarterly (Q1, IF: 2.494), and Digital Journalism (Q1, IF: 4.476) were included in this present study ( Table 1 ).

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https://doi.org/10.1371/journal.pone.0263725.t001

Data collection

All issues of these journals in 2018 were reviewed through the journals’ web pages. All published articles in this year were evaluated on the web pages’ archive. The date range was considered based on published issues in the journals. Only original articles (meta-analyses, systematic reviews, original research articles, and research notes) in 2018 were included in this study. Articles such as editorials, book review articles, case reports, letters to the editor, and other non-research correspondences were excluded.

The findings of the articles (title, doi number, article type) were recorded. In January 2021, the title of the article or doi number was searched one by one in Google Scholar (GS) and Web of Science (WoS) Clarivate. The traditional citation numbers (GS and WoS) of these articles were recorded. The tweet number and social media posts of these articles were searched by their metric evaluating system ( https://www.altmetric.com/ ) and recorded. The data were appraised nearly three years after the articles’ publication date. It was thought that this period would allow the impact of published articles (the citations and Twitter mentions) to be fully observed.

Seven hundred and eleven articles were published in the top ten communication-based journals in 2018. After the exclusion criteria were applied, 572 articles were included for analysis. A total of 570 articles (99.7%) were cited at least once on GS, and 518 articles (90.6%) were cited at least once on WoS. The total cumulative number of citations for all the articles was 21,242 for GS and 5,874 for WoS. The number value of citations ranged from 0 to 868 on GS, and 0–235 on WoS. The median number of citations was 19 citations (interquartile range (IQR): 0–868 citations) for GS, and five citations (IQR: 0–235 citations) for WoS ( Table 2 ).

https://doi.org/10.1371/journal.pone.0263725.t002

In all, 522 articles (91.3%) were posted at least once on Twitter or other platforms. The total number of mentions on all social media platforms was 50,624 items. Overall, the most-used social media platform was Twitter. The majority of articles (n: 500; 87.4%) were mentioned at least once on Twitter, and these articles had cumulatively tweets 13,438 tweet. The median value of Twitter posts was nine tweets (IQR: 0–502 tweet).

The median citation value of articles on GS was 21 citations (IQR: 0–868) for the articles that had been tweeted at least one. However, it was nine (IQR: 0–72) citations for non-tweeted articles. Further, the median WoS citation number was five (IQR: 0–235) for tweeted articles and two (IQR: 0–19) for non-tweeted articles.

The tweeted articles were cited more often than those with no tweets on both platforms (for GS: Mann-Whitney U: 10107, Z: -6.022, p< 0.001; for WoS: Mann-Whitney U: 10547, Z: -5.699, p< 0.001, respectively). This observation was also similar for the other platforms (for GS: Mann-Whitney U: 6493, Z: -5.875, p< 0.001; for WoS: Mann-Whitney U: 6735.5, Z: -5.671, p< 0.001, respectively) ( Table 3 ).

https://doi.org/10.1371/journal.pone.0263725.t003

It was observed that a significant correlation between the number of Twitter posts and the number of citations in GS (r = 0.44, p<0.001) and in WoS (r = 0.50, P<0.001). Similarly, there was also a positive correlation between the number of mentions on all platforms and the number of citations in GS (r = 0.83, p<0.001) and WoS (r = 0.71, p<0.001) ( Fig 1 ).

A. The relationship between metric value of articles and Google scholar citations. B. The relationship between metric value of articles and Web of Science citations. C. The relationship between Twitter posts of articles and Web of Science citations. D. The relationship between Twitter posts of articles and Google Scholar citations.

https://doi.org/10.1371/journal.pone.0263725.g001

Social networks in academia are rapidly improving, and significantly increasing use by the scientific community [ 55 ]. Many journals frequently use these tools for advertising and sharing information [ 56 ]. Social media platforms have brought another dimension to access the information. With developments in social sharing platforms, there has been a transition to the digital age of accessing information [ 57 ]. Thus, these platforms have presented new opportunities for researchers to extend their publications the scientific society [ 36 , 58 ]. Similarly, this present study found that the articles exposed to social media were cited more than the articles not posted on social media. This study showed that the visibility of articles might be increased by sharing them on social media, which allows the real effects of the articles to emerge more rapidly.

Generally, the use of all networks has a similar effect, but the most used platform for this purpose is Twitter. Twitter allows for the rapid sharing of information within seconds of posting a tweet. Thus, the dissemination rate of tweets increases exponentially [ 59 ]. As in other studies, it was found that more use of Twitter than other platforms in this study. Moreover, a positive correlation was observed between the altmetric score, Twitter posts, and citation rate of articles. The articles that were tweeted at least once were cited more than those with no posts on both platforms (for GS, p <0.001; and for WoS, p <0.001). The findings support the conclusion that Twitter activity may reflect the quality of articles or increase their citations. Thus, the measure of social platforms based on tweets should be used to complement the traditional metrics of article citations.

Especially the science of communication is the social sciences field most related to social networks. The traditional publishing continues in the communication sciences, but its adoption of social media-related studies is increasing daily [ 10 , 14 ]. Therefore, sharing and disseminating the articles in communication journals may significantly increase their citation rates. This present study revealed this relationship and highlighted the impact of using social networks in the academic world. Thus, authors and journals should share all articles using social media tools to increase the impact of the article.

The use of social media platforms in the scientific world will make an important contribution to traditional metric systems. Published articles can be posted on social media to reach more people, disseminate information quickly and increase their impact faster [ 60 ]. Further, academic journals’ use of Twitter will promote the journals and increase the citation numbers of the articles [ 9 , 60 ]. Many journals share all of their articles published on their social media accounts. Some journals only share articles they deem important. However, increasing the visibility of articles on social media platforms could be a tool for reaching more people. Thus, this study shows that Twitter posts could mediate these articles to reach more people.

The results of this study show that articles with exposure to social media had higher citation rates. There was also a positive correlation between exposure to social media and article citations. Therefore, the scientist and journals should develop new projects to increase the usage of social media.

However, the present study has some limitations. First, it analyzed only the number of citations and the number of tweets and did not evaluate their content or the Twitter account holders. Second, this study evaluated only the top 10 journals in the communication science. Third, multiple factors (such as sending articles to the press at the same time, focusing on some specific communication topics, and evaluating popular topics) may play a role in the emergence of the articles’ impact. Posting articles on Twitter is only one contributor to this impact. Further, this study examined the relationship between the number of Twitter posts and the number of citations of articles. However, the cause of the relationship was not analyzed. Therefore, more work is needed to explain potential causes of the relationships between posts and citations of an article. Nevertheless, this study presented significant findings that highlight the importance of using social media in academia.

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Open access
Published: 16 March 2020

Exploring the role of social media in collaborative learning the new domain of learning

Jamal Abdul Nasir Ansari 1 &
Nawab Ali Khan 1

Smart Learning Environments volume 7 , Article number: 9 ( 2020 ) Cite this article

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This study is an attempt to examine the application and usefulness of social media and mobile devices in transferring the resources and interaction with academicians in higher education institutions across the boundary wall, a hitherto unexplained area of research. This empirical study is based on the survey of 360 students of a university in eastern India, cognising students’ perception on social media and mobile devices through collaborative learning, interactivity with peers, teachers and its significant impact on students’ academic performance. A latent variance-based structural equation model approach was followed for measurement and instrument validation. The study revealed that online social media used for collaborative learning had a significant impact on interactivity with peers, teachers and online knowledge sharing behaviour.

Additionally, interactivity with teachers, peers, and online knowledge sharing behaviour has seen a significant impact on students’ engagement which consequently has a significant impact on students’ academic performance. Grounded to this finding, it would be valuable to mention that use of online social media for collaborative learning facilitate students to be more creative, dynamic and research-oriented. It is purely a domain of knowledge.

Introduction

The explosion of Information and Communication Technology (ICT) has led to an increase in the volume and smoothness in transferring course contents, which further stimulates the appeasement of Digital Learning Communities (DLCs). The millennium and naughtiness age bracket were Information Technology (IT) centric on web space where individual and geopolitical disperse learners accomplished their e-learning goals. The Educause Center for Applied Research [ECAR] ( 2012 ) surveyed students in higher education mentioned that students are pouring the acceptance of mobile computing devices (cellphones, smartphones, and tablet) in Higher Education Institutions (HEIs), roughly 67% surveyed students accepted that mobile devices and social media play a vital role in their academic performance and career enhancement. Mobile devices and social media provide excellent educational e-learning opportunities to the students for academic collaboration, accessing in course contents, and tutors despite the physical boundary (Gikas & Grant, 2013 ). Electronic communication technologies accelerate the pace of their encroachment of every aspect of life, the educational institutions incessantly long decades to struggle in seeing the role of such devices in sharing the contents, usefulness and interactivity style. Adoption and application of mobile devices and social media can provide ample futuristic learning opportunities to the students in accessing course contents as well as interaction with peers and experts (Cavus & Ibrahim, 2008 , 2009 ; Kukulska-Hulme & Shield, 2008 ; Nihalani & Mayrath, 2010 ; Richardson & Lenarcic, 2008 , Shih, 2007 ). Recently Pew Research Center reported that 55% American teenage age bracket of 15–17 years using online social networking sites, i.e. Myspace and Facebook (Reuben, 2008 ). Social media, the fast triggering the mean of virtual communication, internet-based technologies changed the life pattern of young youth.

Use of social media and mobile devices presents both advantages as well as challenges, mostly its benefits seen in terms of accessing course contents, video clip, transfer of the instructional notes etc. Overall students feel that social media and mobile devices are the cheap and convenient tools of obtaining relevant information. Studies in western countries have confronted that online social media use for collaborative learning has a significant contribution to students’ academic performance and satisfaction (Zhu, 2012 ). The purpose of this research project was to explore how learning and teaching activities in higher education institutions were affected by the integration and application of mobile devices in sharing the resource materials, interaction with colleagues and students’ academic performance. The broad goal of this research was to contemporise the in-depth perspectives of students’ perception of mobile devices and social media in learning and teaching activities. However, this research paper paid attention to only students’ experiences, and their understanding of mobile devices and social media fetched changes and its competency in academic performance. The fundamental research question of this research was, what are the opinions of students on social media and mobile devices when it is integrating into higher education for accessing, interacting with peers.

A researcher of the University of Central Florida reported that electronic devices and social media create an opportunity to the students for collaborative learning and also allowed the students in sharing the resource materials to the colleagues (Gikas & Grant, 2013 ). The result of the eight Egyptian universities confirmed that social media have the significant impact on higher education institutions especially in term of learning tools and teaching aids, faculty members’ use of social media seen at a minimum level due to several barriers (internet accessibility, mobile devices etc.).

Social media and mobile devices allow the students to create, edit and share the course contents in textual, video or audio forms. These technological innovations give birth to a new kind of learning cultures, learning based on the principles of collective exploration and interaction (Selwyn, 2012 ). Social media the phenomena originated in 2005 after the Web2.0 existence into the reality, defined more clearly as “a group of Internet-based applications that build on the ideological and technological foundation of web 2.0 and allow creation and exchange of user-generated contents (Kaplan & Haenlein, 2010 ). Mobile devices and social media provide opportunities to the students for accessing resources, materials, course contents, interaction with mentor and colleagues (Cavus & Ibrahim, 2008 , 2009 ; Richardson & Lenarcic, 2008 ).

Social media platform in academic institutions allows students to interact with their mentors, access their course contents, customisation and build students communities (Greenhow, 2011a , 2011b ). 90% school going students currently utilise the internet consistently, with more than 75% teenagers using online networking sites for e-learning (DeBell & Chapman, 2006 ; Lenhart, Arafeh, & Smith, 2008 ; Lenhart, Madden, & Hitlin, 2005 ). The result of the focus group interview of the students in 3 different universities in the United States confirmed that use of social media created opportunities to the learners for collaborative learning, creating and engaging the students in various extra curriculum activities (Gikas & Grant, 2013 ).

Research background and hypotheses

The technological innovation and increased use of the internet for e-learning by the students in higher education institutions has brought revolutionary changes in communication pattern. A report on 3000 college students in the United States revealed that 90% using Facebook while 37% using Twitter to share the resource materials as cited in (Elkaseh, Wong, & Fung, 2016 ). A study highlighted that the usage of social networking sites in educational institutions has a practical outcome on students’ learning outcomes (Jackson, 2011 ). The empirical investigation over 252 undergraduate students of business and management showed that time spent on twitter and involvement in managing social lives and sharing information, course-related influences their performance (Evans, 2014 ).

Social media for collaborative learning, interactivity with teachers, interactivity with peers

Many kinds of research confronted on the applicability of social media and mobile devices in higher education for interaction with colleagues.90% of faculty members use some social media in courses they were usually teaching or professional purposes out of the campus life. Facebook and YouTube are the most visited sites for the professional outcomes, around 2/3rd of the all-faculty use some medium fora class session, and 30% posted contents for students engagement in reading, view materials (Moran, Seaman, & Tinti-Kane, 2011 ). Use of social media and mobile devices in higher education is relatively new phenomena, completely hitherto area of research. Research on the students of faculty of Economics at University of Mortar, Bosnia, and Herzegovina reported that social media is already used for the sharing the materials and exchanges of information and students are ready for active use of social networking site (slide share etc.) for educational purposes mainly e-learning and communication (Mirela Mabić, 2014 ).

The report published by the U.S. higher education department stated that the majority of the faculty members engaged in different form of the social media for professional purposes, use of social media for teaching international business, sharing contents with the far way students, the use of social media and mobile devices for sharing and the interactive nature of online and mobile technologies build a better learning environment at international level. Responses on 308 graduate and postgraduate students in Saudi Arabia University exhibited that positive correlation between chatting, online discussion and file sharing and knowledge sharing, and entertainment and enjoyment with students learning (Eid & Al-Jabri, 2016 ). The quantitative study on 168 faculty members using partial least square (PLS-SEM) at Carnegie classified Doctoral Research University in the USA confirmed that perceived usefulness, external pressure and compatibility of task-technology have positive effect on social media use, the higher the degree of the perceived risk of social media, the less likely to use the technological tools for classroom instruction, the study further revealed that use of social media for collaborative learning has a positive effect on students learning outcome and satisfaction (Cao, Ajjan, & Hong, 2013 ). Therefore, the authors have hypothesized:

H1: Use of social media for collaborative learning is positively associated with interactivity with teachers.

Additionally, Madden and Zickuhr ( 2011 ) concluded that 83% of internet user within the age bracket of 18–29 years adopting social media for interaction with colleagues. Kabilan, Ahmad, and Abidin ( 2010 ) made an empirical investigation on 300 students at University Sains Malaysia and concluded that 74% students found to be the same view that social media infuses constructive attitude towards learning English (Fig. 1 ).

Research Model

Reuben ( 2008 ) concluded in his study on social media usage among professional institutions revealed that Facebook and YouTube used over half of 148 higher education institutions. Nevertheless, a recent survey of 456 accredited United States institutions highlighted 100% using some form of social media, notably Facebook 98% and Twitter 84% for e-learning purposes, interaction with mentors (Barnes & Lescault, 2011 ).

Information and communication technology (ICT), such as web-based application and social networking sites enhances the collaboration and construction of knowledge byway of instruction with outside experts (Zhu, 2012 ). A positive statistically significant relationship was found between student’s use of a variety of social media tools and the colleague’s fellow as well as the overall quality of experiences (Rutherford, 2010 ). The potential use of social media leads to collaborative learning environments which allow students to share education-related materials and contents (Fisher & Baird, 2006 ). The report of 233 students in the United States higher educations confirmed that more recluse students interact through social media, which assist them in collaborative learning and boosting their self-confidence (Voorn & Kommers, 2013 ). Thus hypotheses as

H2: Use of social media for collaborative learning is positively associated with interactivity with peers.

Social media for collaborative learning, interactivity with peers, online knowledge sharing behaviour and students’ engagement

Students’ engagement in social media and its types represent their physical and mental involvement and time spent boost to the enhancement of educational Excellency, time spent on interaction with peers, teachers for collaborative learning (Kuh, 2007 ). Students’ engagement enhanced when interacting with peers and teacher was in the same direction, shares of ideas (Chickering & Gamson, 1987 ). Engagement is an active state that is influenced by interaction or lack thereof (Leece, 2011 ). With the advancement in information technology, the virtual world becomes the storehouse of the information. Liccardi et al. ( 2007 ) concluded that 30% students were noted to be active on social media for interaction with their colleagues, tutors, and friends while more than 52% used some social media forms for video sharing, blogs, chatting, and wiki during their class time. E-learning becomes now sharp and powerful tools in information technology and makes a substantial impact on the student’s academic performance. Sharing your knowledge will make you better. Social network ties were shown to be the best predictors of online knowledge sharing intention, which in turn associated with knowledge sharing behaviour (Chen, Chen, & Kinshuk, 2009 ). Social media provides the robust personalised, interactive learning environment and enhances in self-motivation as cited in (Al-Mukhaini, Al-Qayoudhi, & Al-Badi, 2014 ). Therefore, it was hypothesised that:

H3: Use of social media for collaborative learning is positively associated with online knowledge sharing behaviour.

Broadly Speaking social media/sites allow the students to interact, share the contents with colleagues, also assisting in building connections with others (Cain, 2008 ). In the present era, the majority of the college-going students are seen to be frequent users of these sophisticated devices to keep them informed and updated about the external affair. Facebook reported per day 1,00,000 new members join; Facebook is the most preferred social networking sites among the students of the United States as cited in (Cain, 2008 ). The researcher of the school of engineering, Swiss Federal Institute of Technology Lausanne, Switzerland, designed and developed Grasp, a social media platform for their students’ collaborative learning, sharing contents (Bogdanov et al., 2012 ). The utility and its usefulness could be seen in the University of Geneva and Tongji University at both two educational places students were satisfied and accept ‘ Grasp’ to collect, organised and share the contents. Students use of social media will interact ubiquity, heterogeneous and engaged in large groups (Wankel, 2009 ). So we hypotheses

H4: More interaction with teachers leads to higher students’ engagement.

However, a similar report published on 233 students revealed that social media assisted in their collaborative learning and self-confidence as they prefer communication technology than face to face communication. Although, the students have the willingness to communicate via social media platform than face to face (Voorn & Kommers, 2013 ). The potential use of social media tools facilitates in achieving higher-level learning through collaboration with colleagues and other renewed experts in their field (Junco, Heiberger, & Loken, 2011 ; Meyer, 2010 ; Novak, Razzouk, & Johnson, 2012 ; Redecker, Ala-Mutka, & Punie, 2010 ). Academic self-efficacy and optimism were found to be strongly related to performance, adjustment and consequently both directly impacted on student’s academic performance (Chemers, Hu, & Garcia, 2001 ). Data of 723 Malaysian researchers confirmed that both male and female students were satisfied with the use of social media for collaborative learning and engagement was found positively affected with learning performance (Al-Rahmi, Alias, Othman, Marin, & Tur, 2018 ). Social media were seen as a powerful driver for learning activities in terms of frankness, interactivity, and friendliness.

Junco et al. ( 2011 ) conducted research on the specific purpose of the social media; how Twitter impacted students’ engagement, found that it was extent discussion out of class, their participation in panel group (Rodriguez, 2011 ). A comparative study conducted by (Roblyer, McDaniel, Webb, Herman, & Witty, 2010 ) revealed that students were more techno-oriented than faculty members and more likely using Facebook and such similar communication technology to support their class-related task. Additionally, faculty members were more likely to use traditional techniques, i.e. email. Thus hypotheses framed is that:

H5: More interaction with peers ultimately leads to better students’ engagement.

Social networking sites and social media are closely similar, which provide a platform where students can interact, communicate, and share emotional intelligence and looking for people with other attitudes (Gikas & Grant, 2013 ). Facebook and YouTube channel use also increased in the skills/ability and knowledge and outcomes (Daniel, Isaac, & Janet, 2017 ). It was highlighted that 90% of faculty members were using some sort of social media in their courses/ teaching. Facebook was the most visited social media sites as per study, 40% of faculty members requested students to read and views content posted on social media; majority reports that videos, wiki, etc. the primary source of acquiring knowledge, social networking sites valuable tool/source of collaborative learning (Moran et al., 2011 ). However, more interestingly, in a study which was carried out on 658 faculty members in the eight different state university of Turkey, concluded that nearly half of the faculty member has some social media accounts.

Further reported that adopting social media for educational purposes, the primary motivational factor which stimulates them to use was effective and quick means of communication technology (Akçayır, 2017 ). Thus hypotheses formulated is:

H6: Online knowledge sharing behaviour is positively associated with the students’ engagement.

Using multiple treatment research design, following act-react to increase students’ academic performance and productivity, it was observed when self–monitoring record sheet was placed before students and seen that students engagement and educational productivity was increased (Rock & Thead, 2007 ). Student engagement in extra curriculum activities promotes academic achievement (Skinner & Belmont, 1993 ), increases grade rate (Connell, Spencer, & Aber, 1994 ), triggering student performance and positive expectations about academic abilities (Skinner & Belmont, 1993 ). They are spending time on online social networking sites linked to students engagement, which works as the motivator of academic performance (Fan & Williams, 2010 ). Moreover, it was noted in a survey of over 236 Malaysian students that weak association found between the online game and student’s academic performance (Eow, Ali, Mahmud, & Baki, 2009 ). In a survey of 671 students in Jordan, it was revealed that student’s engagement directly influences academic performance, also seen the indirect effect of parental involvement over academic performance (Al-Alwan, 2014 ). Engaged students are perceptive and highly active in classroom activities, ready to participate in different classroom extra activities and expose motivation to learn, which finally leads in academic achievement (Reyes, Brackett, Rivers, White, & Salovey, 2012 ). A mediated role of students engagement seen in 1399 students’ classroom emotional climate and grades (Reyes et al., 2012 ). A statistically significant relation was noticed between online lecture and exam performance.

Nonetheless, intelligence quotient, personality factors, students must be engaged in learning activities as cited in (Bertheussen & Myrland, 2016 ). The report of the 1906 students at 7 universities in Colombia confirmed that the weak correlation between collaborative learning, students faculty interaction with academic performance (Pineda-Báez et al., 2014 ) Thus, the hypothesis

H7: Student's Engagement is positively associated with the student's academic performance.

Methodology

To check the students’ perception on social media for collaborative learning in higher education institutions, Data were gathered both offline and online survey administered to students from one public university in Eastern India (BBAU, Lucknow). For the sake of this study, indicators of interactivity with peers and teachers, the items of students engagement, the statement of social media for collaborative learning, and the elements of students’ academic performance were adopted from (AL-Rahmi & Othman, 2013 ). The statement of online knowledge sharing behaviour was taken from (Ma & Yuen, 2011 ).

The indicators of all variables which were mentioned above are measured on the standardised seven-point Likert scale with the anchor (1-Strongly Disagree, to 7-Strongly Agree). Interactivity with peers was measured using four indicators; the sample items using social media in class facilitates interaction with peers ; interactivity with teachers was measured using four symbols, the sample item is using social media in class allows me to discuss with the teacher. ; engagement was measured using three indicators by using social media I felt that my opinions had been taken into account in this class ; social media for collaborative learning was measured using four indicators collaborative learning experience in social media environment is better than in a face-to-face learning environment ; students’ academic performance was measured using five signs using social media to build a student-lecturer relationship with my lecturers, and this improves my academic performance ; online knowledge sharing behaviour was assessed using five symbols the counsel was received from other colleague using social media has increased our experience .

Procedure and measurement

A sample of 360 undergraduate students was collected by convenience sampling method of a public university in Eastern India. The proposed model of study was measured and evaluated using variance based structured equation model (SEM)-a latent multi variance technique which provides the concurrent estimation of structural and measurement model that does not meet parametric assumption (Coelho & Duarte, 2016 ; Haryono & Wardoyo, 2012 ; Lee, 2007 ; Moqbel, Nevo, & Kock, 2013 ; Raykov & Marcoulides, 2000 ; Williams, Rana, & Dwivedi, 2015 ). The confirmatory factor analysis (CFA) was conducted to ensure whether the widely accepted criterion of discriminate and convergent validity met or not. The loading of all the indicators should be 0.50 or more (Field, 2011 ; Hair, Anderson, Tatham, & Black, 1992 ). And it should be statistically significant at least at the 0.05.

Demographic analysis (Table 1 )

The majority of the students in this study were females (50.8%) while male students were only 49.2% with age 15–20 years (71.7%). It could be pointed out at this juncture that the majority of the students (53.9%) in BBAU were joined at least 1–5 academic pages for their getting information, awareness and knowledge. 46.1% of students spent 1–5 h per week on social networking sites for collaborative learning, interaction with teachers at an international level. The different academic pages followed for accessing material, communication with the faculty members stood at 44.4%, there would be various forms of the social networking sites (LinkedIn, Slide Share, YouTube Channel, Researchgate) which provide the facility of online collaborative learning, a platform at which both faculty members and students engaged in learning activities.

As per report (Nasir, Khatoon, & Bharadwaj, 2018 ), most of the social media user in India are college-going students, 33% girls followed by 27% boys students, and this reports also forecasted that India is going to become the highest 370.77 million internet users in 2022. Additionally, the majority of the faculty members use smartphone 44% to connect with the students for sharing material content. Technological advantages were the pivotal motivational force which stimulates faculty members and students to exploits the opportunities of resource materials (Nasir & Khan, 2018 ) (Fig. 2 ).

Reasons for Using Social Media

When the students were asked for what reason did they use social media, it was seen that rarely using for self-promotion, very frequently using for self-education, often used for passing the time with friends, and so many fruitful information the image mentioned above depicting.

Instrument validation

The structural model was applied to scrutinize the potency and statistically significant relationship among unobserved variables. The present measurement model was evaluated using Confirmatory Factor Analysis (CFA), and allied procedures to examine the relationship among hypothetical latent variables has acceptable reliability and validity. This study used both SPSS 20.0 and AMOS to check measurement and structural model (Field, 2013 ; Hair, Anderson, et al., 1992 ; Mooi & Sarstedt, 2011 ; Norusis, 2011 ).

The Confirmatory Factor Analysis (CFA) was conducted to ensure whether the widely accepted criterion of discriminant and convergent validity met or not. The loading of all the indicators should be 0.70 or more it should be statistically significant at least at the 0.05 (Field, 2011 ; Hair, Anderson, et al., 1992 ).

CR or CA-based tests measured the reliability of the proposed measurement model. The CA provides an estimate of the indicators intercorrelation (Henseler & Sarstedt, 2013 . The benchmark limits of the CA is 0.7 or more (Nunnally & Bernstein, 1994 ). As per Table 2 , all latent variables in this study above the recommended threshold limit. Although, Average Variance Extracted (AVE) has also been demonstrated which exceed the benchmark limit 0.5. Thus all the above-specified values revealed that our instrument is valid and effective. (See Table 2 for the additional information) (Table 3 ).

In a nutshell, the measurement model clear numerous stringent tests of convergent validity, discriminant validity, reliability, and absence of multi-collinearity. The finding demonstrated that our model meets widely accepted data validation criteria. (Schumacker & Lomax, 2010 ).

The model fit was evaluated through the Chi-Square/degree of freedom (CMIN/DF), Root Mean Residual (RMR), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Goodness of fit index (GFI) and Tucker-Lewis Index (TLI). The benchmark limit of the CFI, TLI, and GFI 0.90or more (Hair et al., 2016 ; Kock, 2011 ). The model study demonstrated in the table, as mentioned above 4 that the minimum threshold limit was achieved (See Table 4 for additional diagnosis).

Path coefficient of several hypotheses has been demonstrated in Fig. 3 , which is a variable par relationship. β (beta) Coefficients, standardised partial regression coefficients signify the powers of the multivariate relationship among latent variables in the model. Remarkably, it was observed that seven out of the seven proposed hypotheses were accepted and 78% of the explained variance in students’ academic performance, 60% explained variance in interactivity with teachers, 48% variance in interactivity with peers, 43% variance in online knowledge sharing behaviour and 79% variance in students’ engagement. Social media collaborative learning has a significant association with teacher interactivity(β = .693, P < 0.001), demonstrating that there is a direct effect on interaction with the teacher by social media when other variables are controlled. On the other hand, use of social media for collaborative learning has noticed statistically significant positive relationship with peers interactivity (β = .704, p < 0.001) meaning thereby, collaborative learning on social media by university students, leads to the high degree of interaction with peers, colleagues. Implied 10% rise in social media use for learning purposes, expected 7.04% increase in interaction with peers.

Path Diagram

Use of social media for collaborating learning has a significant positive association with online knowledge sharing behaviour (β = .583, p < 0.001), meaning thereby that the more intense use of social media for collaborative learning by university students, the more knowledge sharing between peers and colleagues. Also, interaction with the teacher seen the significant statistical positive association with students engagement (β = .450, p < 0.001), telling that the more conversation with teachers, leads to a high level of students engagement. Similarly, the practical interpretation of this result is that there is an expected 4.5% increase in student’s participation for every 10% increase in interaction with teachers. Interaction with peers has a significant positive association with students engagement (β = .210, p < 0.001). Practically, the finding revealed that 10% upturn in student’s involvement, there is a 2.1% increase in peer’s interaction. There is a significant positive association between online knowledge sharing behaviour and students engagement (β = 0.247, p < 0.001), and finally students engagement has been a statistically significant positive relationship with students’ academic performance (β = .972, p < 0.001), this is the clear indication that more engaged students in collaborative learning via social media leads to better students’ academic performance.

Discussion and implication

There is a continuing discussion in the academic literature that use of such social media and social networking sites would facilitate collaborative learning. It is human psychology generally that such communication media technology seems only for entertainment, but it should be noted here carefully that if such communication technology would be followed with due attention prove productive. It is essential to acknowledge that most university students nowadays adopting social media communication to interact with colleagues, teachers and also making the group be in touch with old friends and even a convenient source of transferring the resources. In the present era, the majority of the university students having diversified social media community groups like Whatsapp, Facebook pages following different academic web pages to upgrade their knowledge.

Practically for every 10% rise in students’ engagement, expected to be 2.1% increase in peer interaction. As the study suggested that students engage in different sites, they start discussing with colleagues. More engaged students in collaborative learning through social media lead better students’ academic performance. The present study revealed that for every 10% increase in student’s engagement, there would be an expected increase in student academic performance at a rate of 9.72. This extensive research finding revealed that the application of online social media would facilitate the students to become more creative, dynamics and connect to the worldwide instructor for collaborative learning.

Accordingly, the use of online social media for collaborative learning, interaction with mentors and colleagues leadbetter student’s engagement which consequently affects student’s academic performance. The higher education authority should provide such a platform which can nurture the student’s intellectual talents. Based on the empirical investigation, it would be said that students’ engagement, social media communication devices facilitate students to retrieve information and interact with others in real-time regarding sharing teaching materials contents. Additionally, such sophisticated communication devices would prove to be more useful to those students who feel too shy in front of peers; teachers may open up on the web for the collaborative learning and teaching in the global scenario and also beneficial for physically challenged students. It would also make sense that intensive use of such sophisticated technology in teaching pedagogical in higher education further facilitates the teachers and students to interact digitally, web-based learning, creating discussion group, etc. The result of this investigation confirmed that use of social media for collaborative learning purposes, interaction with peers, and teacher affect their academic performance positively, meaning at this moment that implementation of such sophisticated communication technology would bring revolutionary, drastic changes in higher education for international collaborative learning (Table 5 ).

Limitations and future direction

Like all the studies, this study is also not exempted from the pitfalls, lacunas, and drawbacks. The first and foremost research limitation is it ignores the addiction of social media; excess use may lead to destruction, deviation from the focal point. The study only confined to only one academic institution. Hence, the finding of the project cannot be generalised as a whole. The significant positive results were found in this study due to the fact that the social media and mobile devices are frequently used by the university going students not only as a means of gratification but also for educational purposes.

Secondly, this study was conducted on university students, ignoring the faculty members, it might be possible that the faculty members would not have been interested in interacting with the students. Thus, future research could be possible towards faculty members in different higher education institutions. To the authors’ best reliance, this is the first and prime study to check the usefulness and applicability of social media in the higher education system in the Indian context.

Concluding observations

Based on the empirical investigation, it could be noted that application and usefulness of the social media in transferring the resource materials, collaborative learning and interaction with the colleagues as well as teachers would facilitate students to be more enthusiastic and dynamic. This study provides guidelines to the corporate world in formulating strategies regarding the use of social media for collaborative learning.

Availability of data and materials

The corresponding author declared here all types of data used in this study available for any clarification. The author of this manuscript ready for any justification regarding the data set. To make publically available of the data used in this study, the seeker must mail to the mentioned email address. The profile of the respondents was completely confidential.

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Understanding the complex links between social media and health behaviour

How are social media influencing vaccination read the full collection.

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Peer review
Fabiana Zollo , professor 1 2 ,
Cornelia Betsch , professor 4 5 ,
Marco Delmastro , research fellow 1 6 ,
1 Ca’ Foscari University of Venice, Venice, Italy
2 New Institute Centre for Environmental Humanities, Venice, Italy
3 City University of London, London, United Kingdom
4 University of Erfurt, Erfurt, Germany
5 Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
6 Enrico Fermi Center for Study and Research, Rome, Italy
7 Sapienza University, Rome, Italy
Correspondence to: F Zollo fabiana.zollo{at}unive.it

Fabiana Zollo and colleagues call for comprehensive, robust research on the influence of social media on health behaviour in order to improve public health responses

Key messages

Monitoring social media is important to understand public perceptions, biases, and false beliefs

Drawing conclusions on how social media affects health behaviour is difficult because measures are unstandardised, sources are limited, and data are incomplete and biased

Rigorous research is needed from varied settings and demographics to improve understanding of the effect of social media on health behaviour

Over 90% of people connected to the internet are active on social media, with a total of 4.76 billion users worldwide in January 2023. 1 The digital revolution has reshaped the news landscape and changed the way users interact with information. Social media’s targeted communication rapidly reaches vast audiences, who in turn actively participate in shaping and engaging with content. This marks a departure from the more passive consumption patterns associated with traditional media.

Over the past few years, social media have emerged as a primary source of news for many people, despite widespread user concerns about potential misinformation ( box 1 ) and the necessity to discern between reliable and untrustworthy information. 4 Data from six continents also indicate a preference among users for content that reflects their reading or viewing history, rather than content selected by journalists, suggesting a shift towards personalised and user driven content curation. In this evolving landscape, celebrities, influencers, and social media personalities are increasingly assuming roles as news sources, especially on platforms such as TikTok, Instagram, and Snapchat.

What is misinformation?

The term “misinformation” is commonly used, yet its definitions can vary between studies, methods, and scholars, leading to disagreements on its precise meaning 2

Misinformation encompasses false, inaccurate, or misleading information, and is often distinguished from disinformation by its lack of deliberate creation and dissemination with the intent to deceive. Classifications may also extend to conspiracy theories and propaganda

Misinformation poses a substantial risk to public health since it can undermine compliance with important public health measures such as vaccination uptake or physical distancing guidelines 3

Public health organisations have recognised the crucial role of social media in shaping the public debate and are working to utilise social media platforms to inform the public, combat misinformation, and improve health knowledge, attitudes, and behaviour. However, causal research on how social media information affects actual health behaviour is inconclusive, primarily because of methodological challenges associated with connecting online activity to offline actions and accurately measuring behavioural outcomes. 5 Thus, exploring the complex relations between information consumption, personal beliefs, and societal effects remains an important area of study. The development of vaccines against covid-19 was accompanied by an infodemic—an overabundance of information, not all of which is accurate. 6 Study of this phenomenon provides useful insight into the interplay between social media and health behaviours and the opportunities and challenges for research and practice.

Access to data on misinformation and health behaviour

Social media have provided unprecedented opportunities through which health information, including misinformation, can be amplified and spread. However, the impact of exposure to and interaction with misinformation on health behaviour remains a subject of debate within the scientific community. 7 While considerable evidence exists from research indicating that misinformation can affect knowledge, attitudes, or behavioural intentions, reaching a consensus in the scientific community on the links between social media and actual health behaviour has been challenging due to lack of data and inherent limitations in study design.

A recent systematic review of randomised controlled trials, for example, highlighted the need for more conceptual and theoretical work on the causal pathways through which misinformation shapes people’s beliefs and behaviours. 8 This influence is often indirect, meaning that exposure to misinformation may affect changes in health behaviour by shaping psychological factors such as beliefs, feelings, and motivations (the so called psychological antecedents) which are commonly used to explain and predict behaviours. However, the roles of potential mediators such as emotions, social norms, and trust are still poorly understood. While all the studies in that review assessed the effect of misinformation on antecedents (intentions, attitudes, and subjective norms), only two of them measured actual behaviour. These studies included behavioural measures of activism, such as the act of signing petitions, yet none examined the effects of misinformation exposure on direct health measures or behaviours, such as vaccination. Indeed, the literature is unclear about the causal effect of individual online activity on behaviour. For example, while some research shows that risk perceptions and vaccination intentions can be affected by short visits to antivaccination websites, 9 exposure to antivaccination comments posted on news stories online appears to have little influence on individuals’ perspectives regarding vaccines, although it could potentially undermine individuals’ trust in important health communication institutions. 10

Furthermore, drawing links or establishing causality is not a trivial endeavour. One important obstacle to understanding the effect of social media on behaviour lies in the challenge of linking online activity with offline behaviour. This difficulty stems from factors such as data scarcity and privacy concerns, particularly regarding personal and sensitive information, that complicate efforts to assess how online interactions translate into real word actions. Establishing a clear connection between information consumption on social media platforms and tangible behavioural outcomes, while excluding the influence of external variables, is a complex task, especially when examining behaviour over medium to long term periods. Examining behaviour in the long term requires longitudinal data, which are often lacking due to the resources (such as costs and time) required for such research. Adding to these challenges is the lack of standardised measures and definitions across studies. As we have seen, misinformation is not unanimously defined ( box 1 ), and health behaviours also encompass a variety of actions—in the covid-19 pandemic alone, behaviours ranged from adherence to nonpharmaceutical measures such as physical distancing to lockdowns, from handwashing to vaccinations. Even with clear definitions, measuring health behaviours reliably and accurately remains a considerable challenge. 11 Many studies rely heavily on self reported data, which may have a low correlation with objectively measured behaviour. Moreover, many differences exist between countries in terms of how data of this nature are collected. This variability makes it difficult to extrapolate definitive findings from different settings and contexts.

The covid-19 pandemic presented a unique opportunity to further investigate the potential effect of infodemics on health behaviour, especially on vaccine hesitancy and refusal. The evidence in the literature paints a complex picture of the relationship between social media misinformation and vaccination. On the one hand, researchers have identified a negative relationship between sharing misinformation online and vaccination uptake in the United States of America. 12 Similarly, a study in the UK and US suggests that exposure to misinformation reduces individuals’ intention to vaccinate for their own and others’ protection. 13 These findings highlight the potentially detrimental effect of misinformation on public health efforts. A large review of 205 articles looked more specifically into conspiracies around vaccination (under review by journal not yet accepted). While some studies showed causal evidence of exposure to conspiracies on vaccination intentions, most studies were correlative and behaviour was not investigated. Thus, the findings of many studies suggest that uncertainty persists on the causality of this relationship. Further investigation into the association between social media behaviour and attitudes towards covid-19 vaccines showed that vaccine hesitancy was associated with interaction and consumption of low quality information online. 14 These results remained significant even after accounting for relevant variables, suggesting that social media behaviour may play an important role in predicting vaccine attitudes. Supporting this finding, a recent systematic review examining the role of social media as a predictor of covid-19 vaccine outcomes showed predominantly negative associations between social media predictors and vaccine perceptions, in particular concerning vaccine hesitancy. However, the evidence suggests a multifaceted landscape, with findings varying across different social media predictors, populations, and platforms. 15 Moreover, while concerns about infodemics shaping individuals’ behavioural intentions are prevalent, some findings suggest a more nuanced reality. Despite the proliferation of information and debate on covid-19 vaccines, the relatively stable and positive trend in vaccine acceptance rates at an aggregated level challenges simplistic explanations about the effect of misinformation. 16

Overall, despite the importance of the effect of social media and misinformation on health behaviour and the extensive assumptions within policy debates, the literature fails to provide definitive conclusions on a clear association between social media and health behaviour. As discussed earlier, measuring behavioural change is challenging due to the scarcity of studies incorporating actual behavioural measures, limitations in laboratory experiments, and difficulties in establishing connections with online activity. Studies are often confined to specific geographical areas, primarily Western countries (notably the US), or limited to specific time periods. In addition, data samples are often constrained by the lack of comprehensive information, such as demographics or geolocation. Furthermore, longitudinal studies are required with extensive access to social media behaviour as well as access to actual behavioural data.

Therefore, further studies are needed to assess the causal effect of social media on offline behaviour. This will require overcoming the ethical issues of data linkage and protection. Such studies will need to integrate social media data with information from different sources, adjusting statistical methods to handle sampling biases, and accounting for the inherent dynamics of social media discussions, which are often characterised by extreme polarisation and user segregation.

Social media dynamics and health

Social media debates are often marked by intense segregation. Users tend to seek out information that aligns with their existing beliefs while dismissing opposing viewpoints. Social media platforms, especially those employing content filtering algorithms, tend to exploit this natural tendency by favouring content aligned with the user’s history and preferences. 17 After all, platforms such as Facebook are built on the foundational unit of the “like,” which represents the most fundamental action a user can take within the environment. Selective exposure to like minded content can contribute to the formation of echo chambers—that is, well separated groups of like minded users—where individuals are surrounded by others who share similar opinions. This phenomenon can act as a breeding ground for the spread of misinformation and hinder its correction. 18 Analysis of Facebook users has shown the existence of opposing and separate communities— provaccine and antivaccine—with the latter group generally being more active. 19 Another study on the public discussion on covid-19 vaccines found similar results, showing users’ inclination to interact with like minded individuals and the presence of segregated communities, with antivaccine groups exhibiting greater cohesiveness and stability over time. 20 Recent research has found that, on Facebook, like minded sources—that is, sources that align with users’ political leanings— are indeed prevalent in what people see on the platform, although they do not seem to affect polarisation. In other words, no measurable effect on polarisation was seen when exposure to content from like minded sources was reduced. 21 Echo chambers and user segregation are crucial factors, as provaccination campaigns, for example, may become confined to individuals who already support vaccination, thus limiting their overall effectiveness. Recent research has explored how users engaged with covid-19 information on social media, and how such engagement changed over time. 22 Despite earlier findings suggesting that false news might spread faster than trustworthy information, analysis of various platforms indicates no substantial difference in the spread of reliable versus questionable information. Posts and interactions with misinformation sources follow similar growth patterns to those of reliable ones, although scaling factors specific to the platform apply. Mainstream platforms and Reddit have a smaller proportion of posts from questionable sources relative to reliable ones, while Gab stands out by notably amplifying posts from questionable sources. These results suggest that the primary drivers behind the spreading of reliable information and misinformation are the specific rules of the platform and the behaviours of groups and individuals engaged in the discussion, rather than the nature of the content.

Opportunities and challenges for using social media to improve health

The public actively engages in public debates through social media platforms based on their prior perceptions and beliefs. Identity is important; the extent to which people identify with their vaccination status is linked to the way the social media platforms are used. People who identify more strongly with being unvaccinated are less likely to use traditional news sources and rely more on information from social media and messaging services. 23 In this context, monitoring social media has become an essential and powerful tool for a dynamic and real time understanding of the information available to large parts of the public, their perceptions, and the presence of biases and false beliefs. The vast amount of data generated online enables the exploration and analysis of sociocognitive factors underlying the consumption and processing of information. When examined and aggregated, these data can provide valuable insights and reveal hidden patterns on people’s perspectives. These insights can, in turn, support public communication efforts, ranging from monitoring public sentiment, concerns, and reactions to helping identify the informational needs of the population. Ultimately, this information can drive the development of recommendations aimed at improving the effectiveness of communication strategies and health measures. For instance, a recent World Health Organization manual offers a guide to addressing the gap between health guidance recommendations and population behaviour using social listening. 24 Social media sources can be used to respond to specific question of concern, such as understanding why a certain community remains undervaccinated despite widespread availability of vaccines and strong recommendations for vaccination. This approach may facilitate a deeper understanding of the information environment of the population, their behaviour in seeking health information, and their health behaviours, thereby enabling the development of tailored strategies and recommendations.

Social media analyses usually rely on large amounts of data. However, it is important to acknowledge that these data may relate to unrepresentative segments of the population. 25 Therefore, it is crucial to pay careful attention to sample creation, which involves selecting a smaller subset of data from a larger population using a predefined selection method. This statistical challenge, known as sample selection bias, must be duly considered when seeking information about the overall population or specific groups who are less inclined to use social media. Although often oversimplified, social media presents a varied landscape, and the extent of sample selection problems may vary across countries and platforms. 26 For instance, Facebook usually covers a broader spectrum of the population in terms of both audience size and diversity of social groups, while X (formerly Twitter) and TikTok predominantly cater to specific subgroups, such as professionals and younger individuals. Additionally, the varying levels of user engagement in actively participating in conversations on social networks through comments, posts, likes, and other forms of interaction can also contribute to sample bias. Combining social data with information from other sources (for example, census data, electoral rolls, surveys, and health data) and employing statistical methods to adjust for sampling biases is thus crucial to obtain solid research outcomes (see, in another context, previous work on the Brexit referendum 27 ). Such considerations are important for health communication campaigns that are inclusive and resonate with target audiences.

Learning the lessons

The covid-19 pandemic has heightened concerns about the potential effect of misinformation in posing risks to public health. Yet, the issue extends beyond the recent crisis, and is important in shaping our response to future pandemics. Ensuring dissemination of accurate information is essential not only to safeguard public health in the present but also to mitigate risks and enhance preparedness for potential future crises. Assessing the effect of social media use on health behaviour is a complex task, with current evidence yet to be consolidated. To avoid biased outcomes, a comprehensive, multidimensional, and causal approach is necessary when investigating the interplay between online information and real world behaviour. Understanding causal relationships and their drivers will allow interventions to be developed to reduce the detrimental effects of online information on health. It is also essential to define clear outcomes. Indeed, online information about health can cover various aspects, including the formation of public opinion, effects on public discourse and agenda setting, interactions between doctors and patients, as well as influences on health behaviours in the short, medium, or long term. 28

Further research is required to identify vulnerable populations and gain a better understanding of sociodemographic and ideological factors influencing users’ behaviour. Additionally, cultural differences in information consumption and behaviours must also be considered to develop targeted and effective interventions and mitigate the influence of health misinformation. 29

Addressing these questions requires robust data and study designs, with collaboration from digital platforms being crucial in accessing such data. A recent cooperation with Meta 30 allowed researchers to conduct multiple experiments and provided extensive access to user data from Facebook and Instagram. However, the success of this model relies entirely on the willingness of social media companies to participate. This highlights the need for an ethical, transparent collaboration and advocates for the democratisation of social media research through equitable data access. 31 Future studies should replicate these efforts in contexts other than politics, such as health, and expand research beyond the US to achieve a more comprehensive understanding of the effect of social media on behaviour globally.

Acknowledgments

AB, FZ, and WQ acknowledge support from the IRIS Infodemic Coalition (UK government, grant No SCH-00001-3391).

Contributors and sources: The authors have collective experience in studying social dynamics and misinformation. AB, FZ, and WQ have expertise in data science and are cofounders of the IRIS Academic Research Group, dedicated to understanding infodemics and fostering healthy information ecosystems through cross disciplinary collaboration. CB specialises in health communication and decision making. MD is an economist with expertise in investigating social issues, including public discourse and health related decisions. All authors contributed to the writing of the paper and developing the list of references. FZ is the guarantor.

Competing interests: We have read and understood the BMJ policy on declaration of interests and have no interests to declare.

Provenance and peer review Commissioned; externally peer reviewed.

This article is part of a collection that was proposed by the Advancing Health Online Initiative (AHO), a consortium of partners including Meta and MSD, and several non-profit collaborators ( https://www.bmj.com/social-media-influencing-vaccination ). Research articles were submitted following invitations by The BMJ and associated BMJ journals, after consideration by an internal BMJ committee. Non-research articles were independently commissioned by The BMJ with advice from Sander van der Linden, Alison Buttenheim, Briony Swire-Thompson, and Charles Shey Wiysonge. Peer review, editing, and decisions to publish articles were carried out by the respective BMJ journals. Emma Veitch was the editor for this collection.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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Social media analytics: a survey of techniques, tools and platforms

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Published: 26 July 2014
Volume 30 , pages 89–116, ( 2015 )

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This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing.

A survey on sentiment analysis methods, applications, and challenges

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1 Introduction

Social media is defined as web-based and mobile-based Internet applications that allow the creation, access and exchange of user-generated content that is ubiquitously accessible (Kaplan and Haenlein 2010 ). Besides social networking media (e.g., Twitter and Facebook), for convenience, we will also use the term ‘social media’ to encompass really simple syndication (RSS) feeds, blogs, wikis and news, all typically yielding unstructured text and accessible through the web. Social media is especially important for research into computational social science that investigates questions (Lazer et al. 2009 ) using quantitative techniques (e.g., computational statistics, machine learning and complexity) and so-called big data for data mining and simulation modeling (Cioffi-Revilla 2010 ).

This has led to numerous data services, tools and analytics platforms. However, this easy availability of social media data for academic research may change significantly due to commercial pressures. In addition, as discussed in Sect. 2 , the tools available to researchers are far from ideal. They either give superficial access to the raw data or (for non-superficial access) require researchers to program analytics in a language such as Java.

1.1 Terminology

We start with definitions of some of the key techniques related to analyzing unstructured textual data:

Natural language processing —(NLP) is a field of computer science, artificial intelligence and linguistics concerned with the interactions between computers and human (natural) languages. Specifically, it is the process of a computer extracting meaningful information from natural language input and/or producing natural language output.

News analytics —the measurement of the various qualitative and quantitative attributes of textual (unstructured data) news stories. Some of these attributes are: sentiment , relevance and novelty .

Opinion mining —opinion mining (sentiment mining, opinion/sentiment extraction) is the area of research that attempts to make automatic systems to determine human opinion from text written in natural language.

Scraping —collecting online data from social media and other Web sites in the form of unstructured text and also known as site scraping, web harvesting and web data extraction.

Sentiment analysis —sentiment analysis refers to the application of natural language processing, computational linguistics and text analytics to identify and extract subjective information in source materials.

Text analytics —involves information retrieval (IR), lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization and predictive analytics.

1.2 Research challenges

Social media scraping and analytics provides a rich source of academic research challenges for social scientists, computer scientists and funding bodies. Challenges include:

Scraping —although social media data is accessible through APIs, due to the commercial value of the data, most of the major sources such as Facebook and Google are making it increasingly difficult for academics to obtain comprehensive access to their ‘raw’ data; very few social data sources provide affordable data offerings to academia and researchers. News services such as Thomson Reuters and Bloomberg typically charge a premium for access to their data. In contrast, Twitter has recently announced the Twitter Data Grants program, where researchers can apply to get access to Twitter’s public tweets and historical data in order to get insights from its massive set of data (Twitter has more than 500 million tweets a day).

Data cleansing —cleaning unstructured textual data (e.g., normalizing text), especially high-frequency streamed real-time data, still presents numerous problems and research challenges.

Holistic data sources —researchers are increasingly bringing together and combining novel data sources: social media data, real-time market & customer data and geospatial data for analysis.

Data protection —once you have created a ‘big data’ resource, the data needs to be secured, ownership and IP issues resolved (i.e., storing scraped data is against most of the publishers’ terms of service), and users provided with different levels of access; otherwise, users may attempt to ‘suck’ all the valuable data from the database.

Data analytics —sophisticated analysis of social media data for opinion mining (e.g., sentiment analysis) still raises a myriad of challenges due to foreign languages, foreign words, slang, spelling errors and the natural evolving of language.

Analytics dashboards —many social media platforms require users to write APIs to access feeds or program analytics models in a programming language, such as Java. While reasonable for computer scientists, these skills are typically beyond most (social science) researchers. Non-programming interfaces are required for giving what might be referred to as ‘deep’ access to ‘raw’ data, for example, configuring APIs, merging social media feeds, combining holistic sources and developing analytical models.

Data visualization —visual representation of data whereby information that has been abstracted in some schematic form with the goal of communicating information clearly and effectively through graphical means. Given the magnitude of the data involved, visualization is becoming increasingly important.

1.3 Social media research and applications

Social media data is clearly the largest, richest and most dynamic evidence base of human behavior, bringing new opportunities to understand individuals, groups and society. Innovative scientists and industry professionals are increasingly finding novel ways of automatically collecting, combining and analyzing this wealth of data. Naturally, doing justice to these pioneering social media applications in a few paragraphs is challenging. Three illustrative areas are: business, bioscience and social science.

The early business adopters of social media analysis were typically companies in retail and finance. Retail companies use social media to harness their brand awareness, product/customer service improvement, advertising/marketing strategies, network structure analysis, news propagation and even fraud detection. In finance, social media is used for measuring market sentiment and news data is used for trading. As an illustration, Bollen et al. ( 2011 ) measured sentiment of random sample of Twitter data, finding that Dow Jones Industrial Average (DJIA) prices are correlated with the Twitter sentiment 2–3 days earlier with 87.6 percent accuracy. Wolfram ( 2010 ) used Twitter data to train a Support Vector Regression (SVR) model to predict prices of individual NASDAQ stocks, finding ‘significant advantage’ for forecasting prices 15 min in the future.

In the biosciences, social media is being used to collect data on large cohorts for behavioral change initiatives and impact monitoring, such as tackling smoking and obesity or monitoring diseases. An example is Penn State University biologists (Salathé et al. 2012 ) who have developed innovative systems and techniques to track the spread of infectious diseases, with the help of news Web sites, blogs and social media.

Computational social science applications include: monitoring public responses to announcements, speeches and events especially political comments and initiatives; insights into community behavior; social media polling of (hard to contact) groups; early detection of emerging events, as with Twitter. For example, Lerman et al. ( 2008 ) use computational linguistics to automatically predict the impact of news on the public perception of political candidates. Yessenov and Misailovic ( 2009 ) use movie review comments to study the effect of various approaches in extracting text features on the accuracy of four machine learning methods—Naive Bayes, Decision Trees, Maximum Entropy and K-Means clustering. Lastly, Karabulut ( 2013 ) found that Facebook’s Gross National Happiness (GNH) exhibits peaks and troughs in-line with major public events in the USA.

1.4 Social media overview

For this paper, we group social media tools into:

Social media data —social media data types (e.g., social network media, wikis, blogs, RSS feeds and news, etc.) and formats (e.g., XML and JSON). This includes data sets and increasingly important real-time data feeds, such as financial data, customer transaction data, telecoms and spatial data.

Social media programmatic access —data services and tools for sourcing and scraping (textual) data from social networking media, wikis, RSS feeds, news, etc. These can be usefully subdivided into:

Data sources, services and tools —where data is accessed by tools which protect the raw data or provide simple analytics. Examples include: Google Trends, SocialMention, SocialPointer and SocialSeek, which provide a stream of information that aggregates various social media feeds.

Data feeds via APIs —where data sets and feeds are accessible via programmable HTTP-based APIs and return tagged data using XML or JSON, etc. Examples include Wikipedia, Twitter and Facebook.

Text cleaning and storage tools —tools for cleaning and storing textual data. Google Refine and DataWrangler are examples for data cleaning.

Text analysis tools —individual or libraries of tools for analyzing social media data once it has been scraped and cleaned. These are mainly natural language processing, analysis and classification tools, which are explained below.

Transformation tools —simple tools that can transform textual input data into tables, maps, charts (line, pie, scatter, bar, etc.), timeline or even motion (animation over timeline), such as Google Fusion Tables, Zoho Reports, Tableau Public or IBM’s Many Eyes.

Analysis tools —more advanced analytics tools for analyzing social data, identifying connections and building networks, such as Gephi (open source) or the Excel plug-in NodeXL.

Social media platforms —environments that provide comprehensive social media data and libraries of tools for analytics. Examples include: Thomson Reuters Machine Readable News, Radian 6 and Lexalytics.

Social network media platforms —platforms that provide data mining and analytics on Twitter, Facebook and a wide range of other social network media sources.

News platforms —platforms such as Thomson Reuters providing commercial news archives/feeds and associated analytics.

2 Social media methodology and critique

The two major impediments to using social media for academic research are firstly access to comprehensive data sets and secondly tools that allow ‘deep’ data analysis without the need to be able to program in a language such as Java. The majority of social media resources are commercial and companies are naturally trying to monetize their data. As discussed, it is important that researchers have access to open-source ‘big’ (social media) data sets and facilities for experimentation. Otherwise, social media research could become the exclusive domain of major companies, government agencies and a privileged set of academic researchers presiding over private data from which they produce papers that cannot be critiqued or replicated. Recently, there has been a modest response, as Twitter and Gnip are piloting a new program for data access, starting with 5 all-access data grants to select applicants.

2.1 Methodology

Research requirements can be grouped into: data, analytics and facilities.

Researchers need online access to historic and real-time social media data, especially the principal sources, to conduct world-leading research:

Social network media —access to comprehensive historic data sets and also real-time access to sources, possibly with a (15 min) time delay, as with Thomson Reuters and Bloomberg financial data.

News data —access to historic data and real-time news data sets, possibly through the concept of ‘educational data licenses’ (cf. software license).

Public data —access to scraped and archived important public data; available through RSS feeds, blogs or open government databases.

Programmable interfaces —researchers also need access to simple application programming interfaces (APIs) to scrape and store other available data sources that may not be automatically collected.

2.1.2 Analytics

Currently, social media data is typically either available via simple general routines or require the researcher to program their analytics in a language such as MATLAB, Java or Python. As discussed above, researchers require:

Analytics dashboards —non-programming interfaces are required for giving what might be termed as ‘deep’ access to ‘raw’ data.

Holistic data analysis —tools are required for combining (and conducting analytics across) multiple social media and other data sets.

Data visualization —researchers also require visualization tools whereby information that has been abstracted can be visualized in some schematic form with the goal of communicating information clearly and effectively through graphical means.

2.1.3 Facilities

Lastly, the sheer volume of social media data being generated argues for national and international facilities to be established to support social media research (cf. Wharton Research Data Services https://wrds-web.wharton.upenn.edu ):

Data storage —the volume of social media data, current and projected, is beyond most individual universities and hence needs to be addressed at a national science foundation level. Storage is required both for principal data sources (e.g., Twitter), but also for sources collected by individual projects and archived for future use by other researchers.

Computational facility —remotely accessible computational facilities are also required for: a) protecting access to the stored data; b) hosting the analytics and visualization tools; and c) providing computational resources such as grids and GPUs required for processing the data at the facility rather than transmitting it across a network.

2.2 Critique

Much needs to be done to support social media research. As discussed, the majority of current social media resources are commercial, expensive and difficult for academics to obtain full access.

In general, access to important sources of social media data is frequently restricted and full commercial access is expensive.

Siloed data —most data sources (e.g., Twitter) have inherently isolated information making it difficult to combine with other data sources.

Holistic data —in contrast, researchers are increasingly interested in accessing, storing and combining novel data sources: social media data, real-time financial market & customer data and geospatial data for analysis. This is currently extremely difficult to do even for Computer Science departments.

2.2.2 Analytics

Analytical tools provided by vendors are often tied to a single data set, maybe limited in analytical capability, and data charges make them expensive to use.

2.2.3 Facilities

There are an increasing number of powerful commercial platforms, such as the ones supplied by SAS and Thomson Reuters, but the charges are largely prohibitive for academic research. Either comparable facilities need to be provided by national science foundations or vendors need to be persuaded to introduce the concept of an ‘educational license.’

3 Social media data

Clearly, there is a large and increasing number of (commercial) services providing access to social networking media (e.g., Twitter, Facebook and Wikipedia) and news services (e.g., Thomson Reuters Machine Readable News). Equivalent major academic services are scarce.We start by discussing types of data and formats produced by these services.

3.1 Types of data

Although we focus on social media, as discussed, researchers are continually finding new and innovative sources of data to bring together and analyze. So when considering textual data analysis, we should consider multiple sources (e.g., social networking media, RSS feeds, blogs and news) supplemented by numeric (financial) data, telecoms data, geospatial data and potentially speech and video data. Using multiple data sources is certainly the future of analytics.

Broadly, data subdivides into:

Historic data sets —previously accumulated and stored social/news, financial and economic data.

Real-time feeds —live data feeds from streamed social media, news services, financial exchanges, telecoms services, GPS devices and speech.

Raw data —unprocessed computer data straight from source that may contain errors or may be unanalyzed.

Cleaned data —correction or removal of erroneous (dirty) data caused by disparities, keying mistakes, missing bits, outliers, etc.

Value-added data —data that has been cleaned, analyzed, tagged and augmented with knowledge.

3.2 Text data formats

The four most common formats used to markup text are: HTML, XML, JSON and CSV.

HTML —HyperText Markup Language (HTML) as well-known is the markup language for web pages and other information that can be viewed in a web browser. HTML consists of HTML elements, which include tags enclosed in angle brackets (e.g., <div>), within the content of the web page.

XML —Extensible Markup Language (XML)—the markup language for structuring textual data using <tag>…<\tag> to define elements.

JSON —JavaScript Object Notation (JSON) is a text-based open standard designed for human-readable data interchange and is derived from JavaScript.

CSV —a comma-separated values (CSV) file contains the values in a table as a series of ASCII text lines organized such that each column value is separated by a comma from the next column’s value and each row starts a new line.

For completeness, HTML and XML are so-called markup languages (markup and content) that define a set of simple syntactic rules for encoding documents in a format both human readable and machine readable. A markup comprises start-tags (e.g., <tag>), content text and end-tags (e.g., </tag>).

Many feeds use JavaScript Object Notation (JSON), the lightweight data-interchange format, based on a subset of the JavaScript Programming Language. JSON is a language-independent text format that uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. JSON’s basic types are: Number, String, Boolean, Array (an ordered sequence of values, comma-separated and enclosed in square brackets) and Object (an unordered collection of key:value pairs). The JSON format is illustrated in Fig. 1 for a query on the Twitter API on the string ‘UCL,’ which returns two ‘text’ results from the Twitter user ‘uclnews.’

JSON Example

Comma-separated values are not a single, well-defined format but rather refer to any text file that: (a) is plain text using a character set such as ASCII, Unicode or EBCDIC; (b) consists of text records (e.g., one record per line); (c) with records divided into fields separated by delimiters (e.g., comma, semicolon and tab); and (d) where every record has the same sequence of fields.

4 Social media providers

Social media data resources broadly subdivide into those providing:

Freely available databases —repositories that can be freely downloaded, e.g., Wikipedia ( http://dumps.wikimedia.org ) and the Enron e-mail data set available via http://www.cs.cmu.edu/~enron/ .

Data access via tools —sources that provide controlled access to their social media data via dedicated tools, both to facilitate easy interrogation and also to stop users ‘sucking’ all the data from the repository. An example is Google’s Trends. These further subdivided into:

Free sources —repositories that are freely accessible, but the tools protect or may limit access to the ‘raw’ data in the repository, such as the range of tools provided by Google.

Commercial sources —data resellers that charge for access to their social media data. Gnip and DataSift provide commercial access to Twitter data through a partnership, and Thomson Reuters to news data.

Data access via APIs —social media data repositories providing programmable HTTP-based access to the data via APIs (e.g., Twitter, Facebook and Wikipedia).

4.1 Open-source databases

A major open source of social media is Wikipedia, which offers free copies of all available content to interested users (Wikimedia Foundation 2014 ). These databases can be used for mirroring, database queries and social media analytics. They include dumps from any Wikimedia Foundation project: http://dumps.wikimedia.org/ , English Wikipedia dumps in SQL and XML: http://dumps.wikimedia.org/enwiki/ , etc.

Another example of freely available data for research is the World Bank data, i.e., the World Bank Databank ( http://databank.worldbank.org/data/databases.aspx ) , which provides over 40 databases, such as Gender Statistics, Health Nutrition and Population Statistics, Global Economic Prospects, World Development Indicators and Global Development Finance, and many others. Most of the databases can be filtered by country/region, series/topics or time (years and quarters). In addition, tools are provided to allow reports to be customized and displayed in table, chart or map formats.

4.2 Data access via tools

As discussed, most commercial services provide access to social media data via online tools, both to control access to the raw data and increasingly to monetize the data.

4.2.1 Freely accessible sources

Google with tools such as Trends and InSights is a good example of this category. Google is the largest ‘scraper’ in the world, but they do their best to ‘discourage’ scraping of their own pages. (For an introduction of how to surreptitious scrape Google—and avoid being ‘banned’—see http://google-scraper.squabbel.com .) Google’s strategy is to provide a wide range of packages, such as Google Analytics, rather than from a researchers’ viewpoint the more useful programmable HTTP-based APIs.

Figure 2 illustrates how Google Trends displays a particular search term, in this case ‘libor.’ Using Google Trends you can compare up to five topics at a time and also see how often those topics have been mentioned and in which geographic regions the topics have been searched for the most.

Google Trends

4.2.2 Commercial sources

There is an increasing number of commercial services that scrape social networking media and then provide paid-for access via simple analytics tools. (The more comprehensive platforms with extensive analytics are reviewed in Sect. 8 .) In addition, companies such as Twitter are both restricting free access to their data and licensing their data to commercial data resellers, such as Gnip and DataSift.

Gnip is the world’s largest provider of social data. Gnip was the first to partner with Twitter to make their social data available, and since then, it was the first to work with Tumblr, Foursquare, WordPress, Disqus, StockTwits and other leading social platforms. Gnip delivers social data to customers in more than 40 countries, and Gnip’s customers deliver social media analytics to more than 95 % of the Fortune 500. Real-time data from Gnip can be delivered as a ‘Firehose’ of every single activity or via PowerTrack, a proprietary filtering tool that allows users to build queries around only the data they need. PowerTrack rules can filter data streams based on keywords, geo boundaries, phrase matches and even the type of content or media in the activity. The company then offers enrichments to these data streams such as Profile Geo (to add significantly more usable geo data for Twitter), URL expansion and language detection to further enhance the value of the data delivered. In addition to real-time data access, the company also offers Historical PowerTrack and Search API access for Twitter which give customers the ability to pull any Tweet since the first message on March 21, 2006.

Gnip provides access to premium (Gnip’s ‘Complete Access’ sources are publishers that have an agreement with Gnip to resell their data) and free data feeds (Gnip’s ‘Managed Public API Access’ sources provide access to normalized and consolidated free data from their APIs, although it requires Gnip’s paid services for the Data Collectors) via its dashboard (see Fig. 3 ). Firstly, the user only sees the feeds in the dashboard that were paid for under a sales agreement. To select a feed, the user clicks on a publisher and then chooses a specific feed from that publisher as shown in Fig. 3 . Different types of feeds serve different types of use cases and correspond to different types of queries and API endpoints on the publisher’s source API. After selecting the feed, the user is assisted by Gnip to configure it with any required parameters before it begins collecting data. This includes adding at least one rule. Under ‘Get Data’ – > ‘Advanced Settings’ you can also configure how often your feed queries the source API for data (the ‘query rate’). Choose between the publisher’s native data format and Gnip’s Activity Streams format (XML for Enterprise Data Collector feeds).

Gnip Dashboard, Publishers and Feeds

4.3 Data feed access via APIs

For researchers, arguably the most useful sources of social media data are those that provide programmable access via APIs, typically using HTTP-based protocols. Given their importance to academics, here, we review individually wikis, social networking media, RSS feeds, news, etc.

4.3.1 Wiki media

Wikipedia (and wikis in general) provides academics with large open-source repositories of user-generated (crowd-sourced) content. What is not widely known is that Wikipedia provides HTTP-based APIs that allows programmable access and searching (i.e., scraping) that returns data in a variety of formats including XML. In fact, the API is not unique to Wikipedia but part of MediaWiki’s ( http://www.mediawiki.org/ ) open-source toolkit and hence can be used with any MediaWiki-based wikis.

The wiki HTTP-based API works by accepting requests containing one or more input arguments and returning strings, often in XML format, that can be parsed and used by the requesting client. Other formats supported include JSON, WDDX, YAML, or PHP serialized. Details can be found at: http://en.wikipedia.org/w/api.php?action=query&list=allcategories&acprop=size&acprefix=hollywood&format=xml .

The HTTP request must contain: a) the requested ‘action,’ such as query, edit or delete operation; b) an authentication request; and c) any other supported actions. For example, the above request returns an XML string listing the first 10 Wikipedia categories with the prefix ‘hollywood.’ Vaswani ( 2011 ) provides a detailed description of how to scrape Wikipedia using an Apache/PHP development environment and an HTTP client capable of transmitting GET and PUT requests and handling responses.

4.3.2 Social networking media

As with Wikipedia, popular social networks, such as Facebook, Twitter and Foursquare, make a proportion of their data accessible via APIs.

Although many social networking media sites provide APIs, not all sites (e.g., Bing, LinkedIn and Skype) provide API access for scraping data. While more and more social networks are shifting to publicly available content, many leading networks are restricting free access, even to academics. For example, Foursquare announced in December 2013 that it will no longer allow private check-ins on iOS 7, and has now partnered with Gnip to provide a continuous stream of anonymized check-in data. The data is available in two packages: the full Firehose access level and a filtered version via Gnip’s PowerTrack service. Here, we briefly discuss the APIs provided by Twitter and Facebook.

4.3.2.1 Twitter

The default account setting keeps users’ Tweets public, although users can protect their Tweets and make them visible only to their approved Twitter followers. However, less than 10 % of all the Twitter accounts are private. Tweets from public accounts (including replies and mentions) are available in JSON format through Twitter’s Search API for batch requests of past data and Streaming API for near real-time data.

Search API —Query Twitter for recent Tweets containing specific keywords. It is part of the Twitter REST API v1.1 (it attempts to comply with the design principles of the REST architectural style, which stands for Representational State Transfer) and requires an authorized application (using oAuth, the open standard for authorization) before retrieving any results from the API.

Streaming API —A real-time stream of Tweets, filtered by user ID, keyword, geographic location or random sampling.

One may retrieve recent Tweets containing particular keywords through Twitter’s Search API (part of REST API v1.1) with the following API call: https://api.twitter.com/1.1/search/tweets.json?q=APPLE and real-time data using the streaming API call: https://stream.twitter.com/1/statuses/sample.json .

Twitter’s Streaming API allows data to be accessed via filtering (by keywords, user IDs or location) or by sampling of all updates from a select amount of users. Default access level ‘Spritzer’ allows sampling of roughly 1 % of all public statuses, with the option to retrieve 10 % of all statuses via the ‘Gardenhose’ access level (more suitable for data mining and research applications). In social media, streaming APIs are often called Firehose—a syndication feed that publishes all public activities as they happen in one big stream. Twitter has recently announced the Twitter Data Grants program, where researchers can apply to get access to Twitter’s public tweets and historical data in order to get insights from its massive set of data (Twitter has more than 500 million tweets a day); research institutions and academics will not get the Firehose access level; instead, they will only get the data set needed for their research project. Researchers can apply for it at the following address: https://engineering.twitter.com/research/data-grants .

Twitter results are stored in a JSON array of objects containing the fields shown in Fig. 4 . The JSON array consists of a list of objects matching the supplied filters and the search string, where each object is a Tweet and its structure is clearly specified by the object’s fields, e.g., ‘created_at’ and ‘from_user’. The example in Fig. 4 consists of the output of calling Twitter’s GET search API via http://search.twitter.com/search.json?q=financial%20times&rpp=1&include_entities=true&result_type=mixed where the parameters specify that the search query is ‘financial times,’ one result per page, each Tweet should have a node called ‘entities’ (i.e., metadata about the Tweet) and list ‘mixed’ results types, i.e., include both popular and real-time results in the response.

Example Output in JSON for Twitter REST API v1

4.3.2.2 Facebook

Facebook’s privacy issues are more complex than Twitter’s, meaning that a lot of status messages are harder to obtain than Tweets, requiring ‘open authorization’ status from users. Facebook currently stores all data as objects Footnote 1 and has a series of APIs, ranging from the Graph and Public Feed APIs to Keyword Insight API. In order to access the properties of an object, its unique ID must be known to make the API call. Facebook’s Search API (part of Facebook’s Graph API) can be accessed by calling https://graph.facebook.com/search?q=QUERY&type=page . The detailed API query format is shown in Fig. 5 . Here, ‘QUERY’ can be replaced by any search term, and ‘page’ can be replaced with ‘post,’ ‘user,’ ‘page,’ ‘event,’ ‘group,’ ‘place,’ ‘checkin,’ ‘location’ or ‘placetopic.’ The results of this search will contain the unique ID for each object. When returning the individual ID for a particular search result, one can use https://graph.facebook.com/ID to obtain further page details such as number of ‘likes.’ This kind of information is of interest to companies when it comes to brand awareness and competition monitoring.

Facebook Graph API Search Query Format

The Facebook Graph API search queries require an access token included in the request. Searching for pages and places requires an ‘app access token’, whereas searching for other types requires a user access token.

Replacing ‘page’ with ‘post’ in the aforementioned search URL will return all public statuses containing this search term. Footnote 2 Batch requests can be sent by following the procedure outlined here: https://developers.facebook.com/docs/reference/api/batch/ . Information on retrieving real-time updates can be found here: https://developers.facebook.com/docs/reference/api/realtime/ . Facebook also returns data in JSON format and so can be retrieved and stored using the same methods as used with data from Twitter, although the fields are different depending on the search type, as illustrated in Fig. 6 .

Facebook Graph API Search Results for q=’Centrica’ and type=’page’

4.3.3 RSS feeds

A large number of Web sites already provide access to content via RSS feeds. This is the syndication standard for publishing regular updates to web-based content based on a type of XML file that resides on an Internet server. For Web sites, RSS feeds can be created manually or automatically (with software).

An RSS Feed Reader reads the RSS feed file, finds what is new converts it to HTML and displays it. The program fragment in Fig. 7 shows the code for the control and channel statements for the RSS feed. The channel statements define the overall feed or channel, one set of channel statements in the RSS file.

Example RSS Feed Control and Channel Statements

4.3.4 Blogs, news groups and chat services

Blog scraping is the process of scanning through a large number of blogs, usually daily, searching for and copying content. This process is conducted through automated software. Figure 8 illustrates example code for Blog Scraping. This involves getting a Web site’s source code via Java’s URL Class, which can eventually be parsed via Regular Expressions to capture the target content.

Example Code for Blog Scraping

4.3.5 News feeds

News feeds are delivered in a variety of textual formats, often as machine-readable XML documents, JSON or CSV files. They include numerical values, tags and other properties that tend to represent underlying news stories. For testing purposes, historical information is often delivered via flat files, while live data for production is processed and delivered through direct data feeds or APIs. Figure 9 shows a snippet of the software calls to retrieve filtered NY Times articles.

Scraping New York Times Articles

Having examined the ‘classic’ social media data feeds, as an illustration of scraping innovative data sources, we will briefly look at geospatial feeds.

4.3.6 Geospatial feeds

Much of the ‘geospatial’ social media data come from mobile devices that generate location- and time-sensitive data. One can differentiate between four types of mobile social media feeds (Kaplan 2012 ):

Location and time sensitive —exchange of messages with relevance for one specific location at one specific point-in time (e.g., Foursquare).

Location sensitive only —exchange of messages with relevance for one specific location, which are tagged to a certain place and read later by others (e.g., Yelp and Qype)

Time sensitive only —transfer of traditional social media applications to mobile devices to increase immediacy (e.g., posting Twitter messages or Facebook status updates)

Neither location or time sensitive —transfer of traditional social media applications to mobile devices (e.g., watching a YouTube video or reading a Wikipedia entry)

With increasingly advanced mobile devices, notably smartphones, the content (photos, SMS messages, etc.) has geographical identification added, called ‘geotagged.’ These geospatial metadata are usually latitude and longitude coordinates, though they can also include altitude, bearing, distance, accuracy data or place names. GeoRSS is an emerging standard to encode the geographic location into a web feed, with two primary encodings: GeoRSS Geography Markup Language (GML) and GeoRSS Simple.

Example tools are GeoNetwork Opensource—a free comprehensive cataloging application for geographically referenced information, and FeedBurner—a web feed provider that can also provide geotagged feeds, if the specified feeds settings allow it.

As an illustration Fig. 10 shows the pseudo-code for analyzing a geospatial feed.

Pseudo-code for Analyzing a Geospatial Feed

5 Text cleaning, tagging and storing

The importance of ‘quality versus quantity’ of data in social media scraping and analytics cannot be overstated (i.e., garbage in and garbage out ). In fact, many details of analytics models are defined by the types and quality of the data. The nature of the data will also influence the database and hardware used.

Naturally, unstructured textual data can be very noisy (i.e., dirty). Hence, data cleaning (or cleansing, scrubbing) is an important area in social media analytics. The process of data cleaning may involve removing typographical errors or validating and correcting values against a known list of entities. Specifically, text may contain misspelled words, quotations, program codes, extra spaces, extra line breaks, special characters, foreign words, etc. So in order to achieve high-quality text mining, it is necessary to conduct data cleaning at the first step: spell checking, removing duplicates, finding and replacing text, changing the case of text, removing spaces and non-printing characters from text, fixing numbers, number signs and outliers, fixing dates and times, transforming and rearranging columns, rows and table data, etc.

Having reviewed the types and sources of raw data, we now turn to ‘cleaning’ or ‘cleansing’ the data to remove incorrect, inconsistent or missing information. Before discussing strategies for data cleaning, it is essential to identify possible data problems (Narang 2009 ):

Missing data —when a piece of information existed but was not included for whatever reason in the raw data supplied. Problems occur with: a) numeric data when ‘blank’ or a missing value is erroneously substituted by ‘zero’ which is then taken (for example) as the current price; and b) textual data when a missing word (like ‘not’) may change the whole meaning of a sentence.

Incorrect data —when a piece of information is incorrectly specified (such as decimal errors in numeric data or wrong word in textual data) or is incorrectly interpreted (such as a system assuming a currency value is in $ when in fact it is in £ or assuming text is in US English rather than UK English).

Inconsistent data —when a piece of information is inconsistently specified. For example, with numeric data, this might be using a mixture of formats for dates: 2012/10/14, 14/10/2012 or 10/14/2012. For textual data, it might be as simple as: using the same word in a mixture of cases, mixing English and French in a text message, or placing Latin quotes in an otherwise English text.

5.1 Cleansing data

A traditional approach to text data cleaning is to ‘pull’ data into a spreadsheet or spreadsheet-like table and then reformat the text. For example, Google Refine Footnote 3 is a standalone desktop application for data cleaning and transformation to various formats. Transformation expressions are written in proprietary Google Refine Expression Language (GREL) or JYTHON (an implementation of the Python programming language written in Java). Figure 11 illustrates text cleansing.

Text Cleansing Pseudo-code

5.2 Tagging unstructured data

Since most of the social media data is generated by humans and therefore is unstructured (i.e., it lacks a pre-defined structure or data model), an algorithm is required to transform it into structured data to gain any insight. Therefore, unstructured data need to be preprocessed, tagged and then parsed in order to quantify/analyze the social media data.

Adding extra information to the data (i.e., tagging the data) can be performed manually or via rules engines, which seek patterns or interpret the data using techniques such as data mining and text analytics. Algorithms exploit the linguistic, auditory and visual structure inherent in all of the forms of human communication. Tagging the unstructured data usually involve tagging the data with metadata or part-of-speech (POS) tagging. Clearly, the unstructured nature of social media data leads to ambiguity and irregularity when it is being processed by a machine in an automatic fashion.

Using a single data set can provide some interesting insights. However, combining more data sets and processing the unstructured data can result in more valuable insights, allowing us to answer questions that were impossible beforehand.

5.3 Storing data

As discussed, the nature of the social media data is highly influential on the design of the database and possibly the supporting hardware. It would also be very important to note that each social platform has very specific (and narrow) rules around how their respective data can be stored and used. These can be found in the Terms of Service for each platform.

For completeness, databases comprise:

Flat file —a flat file is a two-dimensional database (somewhat like a spreadsheet) containing records that have no structured interrelationship, that can be searched sequentially.

Relational database —a database organized as a set of formally described tables to recognize relations between stored items of information, allowing more complex relationships among the data items. Examples are row-based SQL databases and column-based kdb + used in finance.

noSQL databases —a class of database management system (DBMS) identified by its non-adherence to the widely used relational database management system (RDBMS) model. noSQL/newSQL databases are characterized as: being non-relational, distributed, open-source and horizontally scalable.

5.3.1 Apache (noSQL) databases and tools

The growth of ultra-large Web sites such as Facebook and Google has led to the development of noSQL databases as a way of breaking through the speed constraints that relational databases incur. A key driver has been Google’s MapReduce, i.e., the software framework that allows developers to write programs that process massive amounts of unstructured data in parallel across a distributed cluster of processors or stand-alone computers (Chandrasekar and Kowsalya 2011 ). It was developed at Google for indexing Web pages and replaced their original indexing algorithms and heuristics in 2004. The model is inspired by the ‘Map’ and ‘Reduce’ functions commonly used in functional programming. MapReduce (conceptually) takes as input a list of records, and the ‘Map’ computation splits them among the different computers in a cluster. The result of the Map computation is a list of key/value pairs. The corresponding ‘Reduce’ computation takes each set of values that has the same key and combines them into a single value. A MapReduce program is composed of a ‘Map()’ procedure for filtering and sorting and a ‘Reduce()’ procedure for a summary operation (e.g., counting and grouping).

Figure 12 provides a canonical example application of MapReduce. This example is a process to count the appearances of each different word in a set of documents (MapReduce 2011 ).

The Canonical Example Application of MapReduce

5.3.1.1 Apache open-source software

The research community is increasingly using Apache software for social media analytics. Within the Apache Software Foundation, three levels of software are relevant:

Cassandra/hive databases —Apache Cassandra is an open source (noSQL) distributed DBMS providing a structured ‘key-value’ store. Key-value stores allow an application to store its data in a schema-less way. Related noSQL database products include: Apache Hive, Apache Pig and MongoDB, a scalable and high-performance open-source database designed to handle document-oriented storage. Since noSQL databases are ‘structure-less,’ it is necessary to have a companion SQL database to retain and map the structure of the corresponding data.

Hadoop platform —is a Java-based programming framework that supports the processing of large data sets in a distributed computing environment. An application is broken down into numerous small parts (also called fragments or blocks) that can be run on systems with thousands of nodes involving thousands of terabytes of storage.

Mahout —provides implementations of distributed or otherwise scalable analytics (machine learning) algorithms running on the Hadoop platform. Mahout Footnote 4 supports four classes of algorithms: a) clustering (e.g., K-Means, Fuzzy C-Means) that groups text into related groups; b) classification (e.g., Complementary Naive Bayes classifier) that uses supervised learning to classify text; c) frequent itemset mining takes a set of item groups and identifies which individual items usually appear together; and d) recommendation mining (e.g., user- and item-based recommenders) that takes users’ behavior and from that tries to find items users might like.

6 Social media analytics techniques

As discussed, opinion mining (or sentiment analysis) is an attempt to take advantage of the vast amounts of user-generated text and news content online. One of the primary characteristics of such content is its textual disorder and high diversity. Here, natural language processing, computational linguistics and text analytics are deployed to identify and extract subjective information from source text. The general aim is to determine the attitude of a writer (or speaker) with respect to some topic or the overall contextual polarity of a document.

6.1 Computational science techniques

Automated sentiment analysis of digital texts uses elements from machine learning such as latent semantic analysis, support vector machines, bag-of-words model and semantic orientation (Turney 2002 ). In simple terms, the techniques employ three broad areas:

Computational statistics —refers to computationally intensive statistical methods including resampling methods, Markov chain Monte Carlo methods, local regression, kernel density estimation and principal components analysis.

Machine learning —a system capable of the autonomous acquisition and integration of knowledge learnt from experience, analytical observation, etc. (Murphy 2012 ). These sub-symbolic systems further subdivide into:

Supervised learning such as Regression Trees, Discriminant Function Analysis, Support Vector Machines.

Unsupervised learning such as Self-Organizing Maps (SOM), K-Means.

Machine Learning aims to solve the problem of having huge amounts of data with many variables and is commonly used in areas such as pattern recognition (speech, images), financial algorithms (credit scoring, algorithmic trading) (Nuti et al. 2011 ), energy forecasting (load, price) and biology (tumor detection, drug discovery). Figure 13 illustrates the two learning types of machine learning and their algorithm categories.

Machine Learning Overview

Complexity science —complex simulation models of difficult-to-predict systems derived from statistical physics, information theory and nonlinear dynamics. The realm of physicists and mathematicians.

These techniques are deployed in two ways:

Data mining —knowledge discovery that extracts hidden patterns from huge quantities of data, using sophisticated differential equations, heuristics, statistical discriminators (e.g., hidden Markov models), and artificial intelligence machine learning techniques (e.g., neural networks, genetic algorithms and support vector machines).

Simulation modeling —simulation-based analysis that tests hypotheses. Simulation is used to attempt to predict the dynamics of systems so that the validity of the underlying assumption can be tested.

6.1.1 Stream processing

Lastly, we should mention stream processing (Botan et al 2010 ). Increasingly, analytics applications that consume real-time social media, financial ‘ticker’ and sensor networks data need to process high-volume temporal data with low latency. These applications require support for online analysis of rapidly changing data streams. However, traditional database management systems (DBMSs) have no pre-defined notion of time and cannot handle data online in near real time. This has led to the development of Data Stream Management Systems (DSMSs) (Hebrail 2008 )—processing in main memory without storing the data on disk—that handle transient data streams on-line and process continuous queries on these data streams. Example commercial systems include: Oracle CEP engine, StreamBase and Microsoft’s StreamInsight (Chandramouli et al. 2010 ).

6.2 Sentiment analysis

Sentiment is about mining attitudes, emotions, feelings—it is subjective impressions rather than facts. Generally speaking, sentiment analysis aims to determine the attitude expressed by the text writer or speaker with respect to the topic or the overall contextual polarity of a document (Mejova 2009 ). Pang and Lee ( 2008 ) provide a thorough documentation on the fundamentals and approaches of sentiment classification and extraction, including sentiment polarity, degrees of positivity, subjectivity detection, opinion identification, non-factual information, term presence versus frequency, POS (parts of speech), syntax, negation, topic-oriented features and term-based features beyond term unigrams.

6.2.1 Sentiment classification

Sentiment analysis divides into specific subtasks:

Sentiment context —to extract opinion, one needs to know the ‘context’ of the text, which can vary significantly from specialist review portals/feeds to general forums where opinions can cover a spectrum of topics (Westerski 2008 ).

Sentiment level —text analytics can be conducted at the document, sentence or attribute level.

Sentiment subjectivity —deciding whether a given text expresses an opinion or is factual (i.e., without expressing a positive/negative opinion).

Sentiment orientation/polarity —deciding whether an opinion in a text is positive , neutral or negative .

Sentiment strength —deciding the ‘strength’ of an opinion in a text: weak , mild or strong .

Perhaps, the most difficult analysis is identifying sentiment orientation/polarity and strength— positive (wonderful, elegant, amazing, cool), neutral (fine, ok) and negative (horrible, disgusting, poor, flakey, sucks) due to slang.

A popular approach is to assign orientation/polarity scores (+1, 0, −1) to all words: positive opinion (+1), neutral opinion (0) and negative opinion (−1). The overall orientation/polarity score of the text is the sum of orientation scores of all ‘opinion’ words found. However, there are various potential problems in this simplistic approach, such as negation (e.g., there is nothing I hate about this product). One method of estimating sentiment orientation/polarity of the text is pointwise mutual information (PMI) a measure of association used in information theory and statistics.

6.2.2 Supervised learning methods

There are a number of popular computational statistics and machine learning techniques used for sentiment analysis. For a good introduction, see (Khan et al 2010 ). Techniques include:

Naïve Bayes (NB) —a simple probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions (when features are independent of one another within each class).

Maximum entropy (ME) —the probability distribution that best represents the current state of knowledge is the one with largest information-theoretical entropy.

Support vector machines (SVM) —are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.

Logistic regression (LR) model —is a type of regression analysis used for predicting the outcome of a categorical (a variable that can take on a limited number of categories) criterion variable based on one or more predictor variables.

Latent semantic analysis —an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text (Kobayashi and Takeda 2000 ).

The bag-of-words model is a simplifying representation commonly used in natural language processing and IR, where a sentence or a document is represented as an unordered collection of words, disregarding grammar and even word order. This is a model traditionally applied to sentiment analysis thanks to its simplicity.

6.2.2.1 Naïve Bayes classifier (NBC)

As an example of sentiment analysis, we will describe briefly a Naive Bayes classifier (Murphy 2006 ). The Naive Bayes classifier is general purpose, simple to implement and works well for a range of applications. It classifies data in two steps:

Training step —using the training samples, the method estimates the parameters of a probability distribution, assuming features are conditionally independent given the class.

Analysis/testing step —For any unseen test sample, the method computes the posterior probability of that sample belonging to each class. The method then classifies the test sample according to the largest posterior probability.

Using the Naïve Bayes classifier, the classifier calculates the probability for a text to belong to each of the categories you test against. The category with the highest probability for the given text wins:

Figure 14 provides an example of sentiment classification using a Naïve Bayes classifier in Python. There are a number of Naïve Bayes classifier programs available in Java, including the jBNC toolkit ( http://jbnc.sourceforge.net ), WEKA ( www.cs.waikato.ac.nz/ml/weka ) and Alchemy API ( www.alchemyapi.com/api/demo.html ).

Sentiment Classification Example using Python

We next look at the range of Social Media tools available, starting with ‘tools’ and ‘toolkits,’ and in the subsequent chapter at ‘comprehensive’ social media platforms. Since there are a large number of social media textual data services, tools and platforms, we will restrict ourselves examining a few leading examples.

7 Social media analytics tools

Opinion mining tools are crowded with (commercial) providers, most of which are skewed toward sentiment analysis of customer feedback about products and services. Fortunately, there is a vast spectrum of tools for textual analysis ranging from simple open-source tools to libraries, multi-function commercial toolkits and platforms. This section focuses on individual tools and toolkits for scraping, cleaning and analytics, and the next chapter looks at what we call social media platforms that provide both archive data and real-time feeds, and as well as sophisticated analytics tools.

7.1 Scientific programming tools

Popular scientific analytics libraries and tools have been enhanced to provide support for sourcing, searching and analyzing text. Examples include: R—used for statistical programming, MATLAB—used for numeric scientific programming, and Mathematica—used for symbolic scientific programming (computer algebra).

Data processing and data modeling, e.g., regression analysis, are straightforward using MATLAB, which provides time-series analysis, GUI and array-based statistics. MATLAB is significantly faster than the traditional programming languages and can be used for a wide range of applications. Moreover, the exhaustive built-in plotting functions make it a complex analytics toolkit. More computationally powerful algorithms can be developed using it in conjunction with the packages (e.g., FastICA in order to perform independent component analysis).

Python can be used for (natural) language detection, title and content extraction, query matching and, when used in conjunction with a module such as scikit-learn, it can be trained to perform sentiment analysis, e.g., using a Naïve Bayes classifier.

Another example, Apache UIMA (Unstructured Information Management Applications) is an open-source project that analyzes ‘big data’ and discovers information that is relevant to the user.

7.2 Business toolkits

Business Toolkits are commercial suites of tools that allow users to source, search and analyze text for a range of commercial purposes.

SAS Sentiment Analysis Manager, part of the SAS Text Analytics program, can be used for scraping content sources, including mainstream Web sites and social media outlets, as well as internal organizational text sources, and creates reports that describe the expressed feelings of consumers, customers and competitors in real time.

RapidMiner (Hirudkar and Sherekar 2013 ), a popular toolkit offering an open-source Community Edition released under the GNU AGPL and also an Enterprise Edition offered under a commercial license. RapidMiner provides data mining and machine learning procedures including: data loading and transformation (Extract, Transform, Load, a.k.a. ETL), data preprocessing and visualization, modeling, evaluation, and deployment. RapidMiner is written in Java and uses learning schemes and attribute evaluators from the Weka machine learning environment and statistical modeling schemes from the R project.

Other examples are Lexalytics that provides a commercial sentiment analysis engine for many OEM and direct customers; and IBM SPSS Statistics is one of the most used programs for statistical analysis in social science.

7.3 Social media monitoring tools

Social media monitoring tools are sentiment analysis tools for tracking and measuring what people are saying (typically) about a company or its products, or any topic across the web’s social media landscape.

In the area of social media monitoring examples include: Social Mention, ( http://socialmention.com/ ), which provides social media alerts similarly to Google Alerts; Amplified Analytics ( http://www.amplifiedanalytics.com/ ), which focuses on product reviews and marketing information; Lithium Social Media Monitoring; and Trackur, which is an online reputation monitoring tool that tracks what is being said on the Internet.

Google also provides a few useful free tools. Google Trends shows how often a particular search-term input compares to the total search volume. Another tool built around Google Search is Google Alerts—a content change detection tool that provides notifications automatically. Google also acquired FeedBurner—an RSS feeds management—in 2007.

7.4 Text analysis tools

Text analysis tools are broad-based tools for natural language processing and text analysis. Examples of companies in the text analysis area include: OpenAmplify and Jodange whose tools automatically filter and aggregate thoughts, feelings and statements from traditional and social media.

There are also a large number of freely available tools produced by academic groups and non-governmental organizations (NGO) for sourcing, searching and analyzing opinions. Examples include Stanford NLP group tools and LingPipe, a suite of Java libraries for the linguistic analysis of human language (Teufl et al 2010 ).

A variety of open-source text analytics tools are available, especially for sentiment analysis. A popular text analysis tool, which is also open source, is Python NLTK—Natural Language Toolkit ( www.nltk.org/ ), which includes open-source Python modules, linguistic data and documentation for text analytics. Another one is GATE ( http://gate.ac.uk/sentiment ).

We should also mention Lexalytics Sentiment Toolkit which performs automatic sentiment analysis on input documents. It is powerful when used on a large number of documents, but it does not perform data scraping.

Other commercial software for text mining include: AeroText, Attensity, Clarabridge, IBM LanguageWare, SPSS Text Analytics for Surveys, Language Computer Corporation, STATISTICA Text Miner and WordStat.

7.5 Data visualization tools

The data visualization tools provide business intelligence (BI) capabilities and allow different types of users to gain insights from the ‘big’ data. The users can perform exploratory analysis through interactive user interfaces available on the majority of devices, with a recent focus on mobile devices (smartphones and tablets). The data visualization tools help the users identify patterns, trends and relationships in the data which were previously latent. Fast ad hoc visualization on the data can reveal patterns and outliers, and it can be performed on large-scale data sets frameworks, such as Apache Hadoop or Amazon Kinesis. Two notable visualization tools are SAS Visual Analytics and Tableau.

7.6 Case study: SAS Sentiment Analysis and Social Media Analytics

SAS is the leading advanced analytics software for BI, data management and predictive analytics. SAS Sentiment Analysis (SAS Institute 2013 ) automatically rates and classifies opinions. It also performs data scraping from Web sites, social media and internal file systems. Then, it processes in a unified format to evaluate relevance with regard to its pre-defined topics. SAS Sentiment Analysis identifies trends and emotional changes. Experts can refine the sentiment models through an interactive workbench. The tool automatically assigns sentiment scores to the input documents as they are retrieved in real time.

SAS Sentiment Analysis combines statistical modeling and linguistics (rule-based natural language processing techniques) in order to output accurate sentiment analysis results. The tool monitors and evaluates sentiment changes over time; it extracts sentiments in real time as the scraped data is being retrieved and generates reports showing patterns and detailed reactions.

The software identifies where (i.e., on what channel) the topic is being discussed and quantifies perceptions in the market as the software scrapes and analyzes both internal and external content about your organization (or the concept you are analyzing) and competitors, identifying positive, neutral, negative or ‘no sentiment’ texts in real time.

SAS Sentiment Analysis and SAS Social Media Analytics have a user-friendly interface for developing models; users can upload sentiment analysis models directly to the server in order to minimize the manual model deployment. More advanced users can use the interactive workbench to refine their models. The software includes graphics to illustrate instantaneously the text classification (i.e., positive, negative, neutral or unclassified) and point-and-click exploration in order to drill the classified text into detail. The tool also provides some workbench functionality through APIs, allowing for automatic/programmatic integration with other modules/projects. Figure 15 illustrates the SAS Social Media Analytics graphical reports, which provide user-friendly sentiment insights. The SAS software has crawling plugins for the most popular social media sites, including Facebook, Twitter, Bing, LinkedIn, Flickr and Google. It can also be customized to crawl any Web site using the mark-up matcher; this provides a point-and-click interface to indicate what areas need to be extracted from an HTML or XML. SAS Social Media Analytics gathers online conversations from popular networking sites (e.g., Facebook and Twitter), blogs and review sites (e.g., TripAdvisor and Priceline), and scores the data for influence and sentiment. It provides visualization tools for real-time tracking; it allows users to submit customized queries and returns a geographical visualization with brand-specific commentary from Twitter, as illustrated in Fig. 16 .

Graphical Reports with Sentiment Insights

SAS Visualization of Real-Time Tracking via Twitter

8 Social media analytics platforms

Here, we examine comprehensive social media platforms that combine social media archives, data feeds, data mining and data analysis tools. Simply put, the platforms are different from tools and toolkits since platforms are more comprehensive and provide both tools and data.

They broadly subdivide into:

News platforms —platforms such as Thomson Reuters providing news archives/feeds and associated analytics and targeting companies such as financial institutions seeking to monitor market sentiment in news.

Social network media platforms —platforms that provide data mining and analytics on Twitter, Facebook and a wide range of other social network media sources. Providers typically target companies seeking to monitor sentiment around their brands or products.

8.1 News platforms

The two most prominent business news feed providers are Thomson Reuters and Bloomberg.

Computer read news in real time and provide automatically key indicators and meaningful insights. The news items are automatically retrieved, analyzed and interpreted in a few milliseconds. The machine-readable news indicators can potentially improve quantitative strategies, risk management and decision making.

Examples of machine-readable news include: Thomson Reuters Machine Readable News, Bloomberg’s Event-Driven Trading Feed and AlphaFlash (Deutsche Börse’s machine-readable news feed). Thomson Reuters Machine Readable News (Thomson Reuters 2012a , b , c ) has Reuters News content dating back to 1987, and comprehensive news from over 50 third-parties dating back to 2003, such as PR Newswire, Business Wire and the Regulatory News Service (LSE). The feed offers full text and comprehensive metadata via streaming XML.

Thomson Reuters News Analytics uses Natural Language Processing (NLP) techniques to score news items on tens of thousands of companies and nearly 40 commodities and energy topics. Items are measured across the following dimensions:

Author sentiment —metrics for how positive, negative or neutral the tone of the item is, specific to each company in the article.

Relevance —how relevant or substantive the story is for a particular item.

Volume analysis —how much news is happening on a particular company.

Uniqueness —how new or repetitive the item is over various time periods.

Headline analysis —denotes special features such as broker actions, pricing commentary, interviews, exclusives and wrap-ups.

8.2 Social network media platforms

Attensity, Brandwatch, Salesforce Marketing Cloud (previously called Radian6) and Sysomos MAP (Media Analysis Platform) are examples of social media monitoring platforms, which measure demographics, influential topics and sentiments. They include text analytics and sentiment analysis on online consumer conversations and provide user-friendly interfaces for customizing the search query, dashboards, reports and file export features (e.g., to Excel or CSV format). Most of the platforms scrape a range of social network media using a distributed crawler that targets: micro-blogging (Twitter via full Twitter Firehose), blogs (Blogger, WordPress, etc.), social networks (Facebook and MySpace), forums, news sites, images sites (Flickr) and corporate sites. Some of the platforms provide multi-language support for widely used languages (e.g., English, French, German, Italian and Spanish).

Sentiment analysis platforms use two main methodologies. One involves a statistical or model-based approach wherein the system learns to assess sentiment by analyzing large quantities of pre-scored material. The other method utilizes a large dictionary of pre-scored phrases.

RapidMiner Footnote 5 is a platform which combines data mining and data analysis, which, depending on the requirements, can be open source. It uses the WEKA machine learning library and provides access to data sources such as Excel, Access, Oracle, IBM, MySQL, PostgreSQL and Text files.

Mozenda provides a point-and-click user interface for extracting specific information from the Web sites and allows automation and data export to CSV, TSV or XML files.

DataSift provides access to both real-time and historical social data from the leading social networks and millions of other sources, enabling clients to aggregate, filter and gain insights and discover trends from the billions of public social conversations. Once the data is aggregated and processed (i.e., DataSift can filter and add context, such as enrichments—language processing, geodata and demographics—and categorization—spam detection, intent identification and machine learning), the customers can use pre-built integrations with popular BI tools, application and developer tools to deliver the data into their businesses, or use the DataSift APIs to stream real-time data into their applications.

There are a growing number of social media analytics platforms being founded nowadays. Other notable platforms that handle sentiment and semantic analysis of Web and Web 2.0-sourced material include Google Analytics, HP Autonomy IDOL (Intelligent Data Operating Layer), IBM SPSS Modeler, Adobe SocialAnalytics, GraphDive, Keen IO, Mass Relevance, Parse.ly, ViralHeat, Socialbakers, DachisGroup, evolve24, OpenAmplify and AdmantX.

Recently, more and more specific social analytics platforms have emerged. One of them is iSpot.tv which launched its own social media analytics platform that matches television ads with mentions on Twitter and Facebook. It provides real-time reports about when and where an ad appears, together with what people are saying about it on social networks (iSpot.tv monitors almost 80 different networks).

Thomson Reuters has recently announced that it is now incorporating Twitter sentiment analysis for the Thomson Reuters Eikon market analysis and trading platform, providing visualizations and charts based on the sentiment data. In the previous year, Bloomberg incorporated tweets related to specific companies in a wider data stream.

8.3 Case study: Thomson Reuters News Analytics

Thomson Reuters News Analytics (TRNA) provides a huge news archive with analytics to read and interpret news, offering meaningful insights. TRNA scores news items on over 25,000 equities and nearly 40 topics (commodities and energy). The platform scrapes and analyzes news data in real time and feeds the data into other programs/projects or quantitative strategies.

TRNA uses an NLP system from Lexalytics, one of the linguistics technology leaders, that can track news sentiment over time, and scores text across the various dimensions as mentioned in Sect. 8.1 .

The platform’s text scoring and metadata has more than 80 fields (Thomson Reuters 2010 ) such as:

Item type —stage of the story: Alert, Article, Updates or Corrections.

Item genre —classification of the story, i.e., interview, exclusive and wrap-up.

Headline —alert or headline text.

Relevance —varies from 0 to 1.0.

Prevailing sentiment —can be 1, 0 or −1.

Positive, neutral, negative —more detailed sentiment indication.

Broker action —denotes broker actions: upgrade, downgrade, maintain, undefined or whether it is the broker itself

Price/market commentary —used to flag items describing pricing/market commentary

Topic codes —describes what the story is about, i.e., RCH = Research, RES = Results, RESF = Results Forecast, MRG = Mergers and Acquisitions

A snippet of the news sentiment analysis is illustrated in Fig. 17 .

Thomson Reuters News Discovery Application with Sentiment Analysis

In 2012, Thomson Reuters extended its machine-readable news offering to include sentiment analysis and scoring for social media. TRNA’s extension is called Thomson Reuters News Analytics (TRNA) for Internet News and Social Media, which aggregates content from over four million social media channels and 50,000 Internet news sites. The content is then analyzed by TRNA in real time, generating a quantifiable output across dimensions such as sentiment, relevance, novelty volume, category and source ranks. This extension uses the same extensive metadata tagging (across more than 80 fields).

TRNA for Internet News and Social Media is a powerful platform analyzing, tagging and filtering millions of public and premium sources of Internet content, turning big data into actionable ideas. The platform also provides a way to visually analyze the big data. It can be combined with Panopticon Data Visualization Software in order to reach meaningful conclusions more quickly with visually intuitive displays (Thomson Reuters 2012a , b , c ), as illustrated in Fig. 18 .

Combining TRNA for Internet News and Social Media with Panopticon Data Visualization Software

Thomson Reuters also expanded the News Analytics service with MarketPsych Indices (Thomson Reuters 2012a , b , c ), which allows for real-time psychological analysis of news and social media. The Thomson Reuters MarketPsych Indices (TRMI) service gains a quantitative view of market psychology as it attempts to identify human emotion and sentiment. It is a complement to TRNA and uses NLP processing created by MarketPsych ( http://www.marketpsych.com ), a leading company in behavioral psychology in financial markets.

Behavioral economists have extensively investigated whether emotions affect markets in predictable ways, and TRMI attempts to measure the state of ‘emotions’ in real time in order to identify patterns as they emerge. TRMI has two key indicator types:

Emotional indicators (sentiments) —emotions such as Gloom , Fear , Trust , Uncertainty , Innovation , Anger , Stress , Urgency , Optimism and Joy .

Buzz metrics —they indicate how much something is being discussed in the news and social media and include macroeconomic themes (e.g., Litigation, Mergers, Volatility, Financials sector, Airlines sector and Clean Technology sector )

The platform from Thomson Reuters allows the exploitation of news and social media to be used to spot opportunities and capitalize on market inefficiencies (Thomson Reuters 2013 ).

9 Experimental computational environment for social media

As we have discussed in Sect. 2 methodology and critique, researchers arguably require a comprehensive experimental computational environment/facility for social media research with the following attributes:

Data scraping —the ability through easily programmable APIs to scrape any type of social media (social networking media, RSS feeds, blogs, wikis, news, etc.).

Data streaming —to access and combine real-time feeds and archived data for analytics.

Data storage —a major facility for storing principal data sources and for archiving data collected for specific projects.

Data protection/security —the stored data needs to be protected to stop users attempting to ‘suck it out’ off the facility. Access to certain data sets may need to be restricted and charges may be levied on access (cf. Wharton Research Data Services).

Programmable interfaces —researchers need access to simple application programming interfaces (APIs) to scrape and store other available data sources that may not be automatically collected.

9.2 Analytics

Analytics dashboards —non-programming interfaces are required for giving what might be referred to as ‘deep’ access to ‘raw’ data.

Programmable analytics —programming interfaces are also required so users can deploy advanced data mining and computer simulation models using MATLAB, Java and Python.

Stream processing —facilities are required to support analytics on streamed real-time data feeds, such as Twitter feeds, news feeds and financial tick data.

High-performance computing —lastly the environment needs to support non-programming interfaces to MapReduce/Hadoop, NoSQL databases and Grids of processors.

Decentralized analytics —if researchers are to combine social media data with highly sensitive/valuable proprietary data held by governments, financial institutions, retailers and other commercial organizations, then the environment needs in the future to support decentralized analytics across distributed data sources and in a highly secure way.

Realistically, this is best facilitated at a national or international level.

To provide some insight into the structure of an experimental computational environment for social media (analytics), below we present the system architecture of the UCL SocialSTORM analytics platform developed by Dr. Michal Galas and his colleagues (Galas et al. 2012 ) to University College London (UCL).

University College London’s social media streaming, storage and analytics platform (SocialSTORM) is a cloud-based ‘central hub’ platform, which facilitates the acquisition of text-based data from online sources such as Twitter, Facebook, RSS media and news. The system includes facilities to upload and run Java-coded simulation models to analyze the aggregated data, which may comprise scraped social data and/or users’ own proprietary data.

9.3 System architecture

Figure 19 shows the architecture of the SocialSTORM platform, and the following section outlines the key components of the overall system. The basic idea is that each external feed has a dedicated connectivity engine (API) and this streams data to the message bus, which handles internal communication, analytics and storage.

SocialSTORM Platform Architecture

Connectivity engines —the connectivity modules communicate with the external data sources, including Twitter and Facebook’s APIs, financial blogs, various RSS and news feeds. The platform’s APIs are continually being expanded to incorporate other social media sources as required. Data is fed into SocialSTORM in real time, including a random sample of all public updates from Twitter, providing gigabytes of text-based data every day.

Messaging bus —the message bus serves as the internal communication layer which accepts the incoming data streams (messages) from the various connectivity engines, parses these (from either JSON or XML format) to an internal representation of data in the platform, distributes the information across all the interested modules and writes the various data to the appropriate tables of the main database.

Data warehouse —the database supports terabytes of text-based entries, which are accompanied by various types of metadata to expand the potential avenues of research. Entries are organized by source and accurately time-stamped with the time of publication, as well as being tagged with topics for easy retrieval by simulation models. The platform currently uses HBase, but in future might use Apache Cassandra or Hive.

Simulation manager —the simulation manager provides an external API for clients to interact with the data for research purposes, including a web-based GUI whereby users can select various filters to apply to the data sets before uploading a Java-coded simulation model to perform the desired analysis on the data. This facilitates all client-access to the data warehouse and also allows users to upload their own data sets for aggregation with UCL’s social data for a particular simulation. There is also the option to switch between historical mode (which mines data existing at the time the simulation is started) and live mode (which ‘listens’ to incoming data streams and performs analysis in real time).

9.4 Platform components

The platform comprises the following modules, which are illustrated in Fig. 20 :

Environment System Architecture and Modules

Back-end services —this provides the core of the platform functionalities. It is a set of services that allow connections to data providers, propagation processing and aggregation of data feeds, execution and maintenance of models, as well as their management in a multiuser environment.

Front-end client APIs —this provides a set of programmatic and graphical interfaces that can be used to interact with a platform to implement and test analytical models. The programmatic access provides model templates to simplify access to some of the functionalities and defines generic structure of every model in the platform. The graphic user interface allows visual management of analytical models. It enables the user to visualize data in various forms, provides data watch grid capabilities, provides a dynamic visualization of group behavior of data and allows users to observe information on events relevant to the user’s environment.

Connectivity engine —this functionality provides a means of communication with the outside world, with financial brokers, data providers and others. Each of the outside venues utilized by the platform has a dedicated connector object responsible for control of communication. This is possible due to the fact that each of the outside institutions provide either a dedicated API or is using a communication protocol (e.g., the FIX protocol and the JSON/XML-based protocol). The platform provides a generalized interface to allow standardization of a variety of connectors.

Internal communication layer —the idea behind the use of the internal messaging system in the platform draws from the concept of event-driven programming. Analytical platforms utilize events as a main means of communication between their elements. The elements, in turn, are either producers or consumers of the events. The approach significantly simplifies the architecture of such system while making it scalable and flexible for further extensions.

Aggregation database —this provides a fast and robust DBMS functionality, for an entry-level aggregation of data, which is then filtered, enriched, restructured and stored in big data facilities. Aggregation facilities enable analytical platforms to store, extract and manipulate large amounts of data. The storage capabilities of the Aggregation element not only allow replay of historical data for modeling purposes, but also enable other, more sophisticated tasks related to functioning of the platform including model risk analysis, evaluation of performance of models and many more.

Client SDK —this is a complete set of APIs (Application Programming Interfaces) that enable development, implementation and testing of new analytical models with use of the developer’s favorite IDE (Integrated Development Environment). The SDK allows connection from the IDE to the server side of the platform to provide all the functionalities the user may need to develop and execute models.

Shared memory —this provides a buffer-type functionality that speeds up the delivery of temporal/historical data to models and the analytics-related elements of the platform (i.e., the statistical analysis library of methods), and, at the same time, reduces the memory usage requirement. The main idea is to have a central point in the memory (RAM) of the platform that will manage and provide a temporal/historical data from the current point of time up to a specified number of timestamps back in history). Since the memory is shared, no model will have to keep and manage history by itself. Moreover, since the memory is kept in RAM rather than in the files or the DBMS, the access to it is instant and bounded only by the performance of hardware and the platform on which the buffers work.

Model templates —the platform supports two generic types of models: push and pull. The push type registers itself to listen to a specified set of data streams during initialization, and the execution of the model logic is triggered each time a new data feed arrives to the platform. This type is dedicated to very quick, low-latency, high-frequency models and the speed is achieved at the cost of small shared memory buffers. The pull model template executes and requests data on its own, based on a schedule. Instead of using the memory buffers, it has a direct connection to the big data facilities and hence can request as much historical data as necessary, at the expense of speed.

10 Conclusions

As discussed, the easy availability of APIs provided by Twitter, Facebook and News services has led to an ‘explosion’ of data services and software tools for scraping and sentiment analysis, and social media analytics platforms. This paper surveys some of the social media software tools, and for completeness introduced social media scraping, data cleaning and sentiment analysis.

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Acknowledgments

The authors would like to acknowledge Michal Galas who led the design and implementation of the UCL SocialSTORM platform with the assistance of Ilya Zheludev, Kacper Chwialkowski and Dan Brown. Dr. Christian Hesse of Deutsche Bank is also acknowledged for collaboration on News Analytics.

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Batrinca, B., Treleaven, P.C. Social media analytics: a survey of techniques, tools and platforms. AI & Soc 30 , 89–116 (2015). https://doi.org/10.1007/s00146-014-0549-4

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Front Psychol

The effect of social media on the development of students’ affective variables

1 Science and Technology Department, Nanjing University of Posts and Telecommunications, Nanjing, China

2 School of Marxism, Hohai University, Nanjing, Jiangsu, China

3 Government Enterprise Customer Center, China Mobile Group Jiangsu Co., Ltd., Nanjing, China

The use of social media is incomparably on the rise among students, influenced by the globalized forms of communication and the post-pandemic rush to use multiple social media platforms for education in different fields of study. Though social media has created tremendous chances for sharing ideas and emotions, the kind of social support it provides might fail to meet students’ emotional needs, or the alleged positive effects might be short-lasting. In recent years, several studies have been conducted to explore the potential effects of social media on students’ affective traits, such as stress, anxiety, depression, and so on. The present paper reviews the findings of the exemplary published works of research to shed light on the positive and negative potential effects of the massive use of social media on students’ emotional well-being. This review can be insightful for teachers who tend to take the potential psychological effects of social media for granted. They may want to know more about the actual effects of the over-reliance on and the excessive (and actually obsessive) use of social media on students’ developing certain images of self and certain emotions which are not necessarily positive. There will be implications for pre- and in-service teacher training and professional development programs and all those involved in student affairs.

Introduction

Social media has turned into an essential element of individuals’ lives including students in today’s world of communication. Its use is growing significantly more than ever before especially in the post-pandemic era, marked by a great revolution happening to the educational systems. Recent investigations of using social media show that approximately 3 billion individuals worldwide are now communicating via social media ( Iwamoto and Chun, 2020 ). This growing population of social media users is spending more and more time on social network groupings, as facts and figures show that individuals spend 2 h a day, on average, on a variety of social media applications, exchanging pictures and messages, updating status, tweeting, favoring, and commenting on many updated socially shared information ( Abbott, 2017 ).

Researchers have begun to investigate the psychological effects of using social media on students’ lives. Chukwuere and Chukwuere (2017) maintained that social media platforms can be considered the most important source of changing individuals’ mood, because when someone is passively using a social media platform seemingly with no special purpose, s/he can finally feel that his/her mood has changed as a function of the nature of content overviewed. Therefore, positive and negative moods can easily be transferred among the population using social media networks ( Chukwuere and Chukwuere, 2017 ). This may become increasingly important as students are seen to be using social media platforms more than before and social networking is becoming an integral aspect of their lives. As described by Iwamoto and Chun (2020) , when students are affected by social media posts, especially due to the increasing reliance on social media use in life, they may be encouraged to begin comparing themselves to others or develop great unrealistic expectations of themselves or others, which can have several affective consequences.

Considering the increasing influence of social media on education, the present paper aims to focus on the affective variables such as depression, stress, and anxiety, and how social media can possibly increase or decrease these emotions in student life. The exemplary works of research on this topic in recent years will be reviewed here, hoping to shed light on the positive and negative effects of these ever-growing influential platforms on the psychology of students.

Significance of the study

Though social media, as the name suggests, is expected to keep people connected, probably this social connection is only superficial, and not adequately deep and meaningful to help individuals feel emotionally attached to others. The psychological effects of social media on student life need to be studied in more depth to see whether social media really acts as a social support for students and whether students can use social media to cope with negative emotions and develop positive feelings or not. In other words, knowledge of the potential effects of the growing use of social media on students’ emotional well-being can bridge the gap between the alleged promises of social media and what it actually has to offer to students in terms of self-concept, self-respect, social role, and coping strategies (for stress, anxiety, etc.).

Exemplary general literature on psychological effects of social media

Before getting down to the effects of social media on students’ emotional well-being, some exemplary works of research in recent years on the topic among general populations are reviewed. For one, Aalbers et al. (2018) reported that individuals who spent more time passively working with social media suffered from more intense levels of hopelessness, loneliness, depression, and perceived inferiority. For another, Tang et al. (2013) observed that the procedures of sharing information, commenting, showing likes and dislikes, posting messages, and doing other common activities on social media are correlated with higher stress. Similarly, Ley et al. (2014) described that people who spend 2 h, on average, on social media applications will face many tragic news, posts, and stories which can raise the total intensity of their stress. This stress-provoking effect of social media has been also pinpointed by Weng and Menczer (2015) , who contended that social media becomes a main source of stress because people often share all kinds of posts, comments, and stories ranging from politics and economics, to personal and social affairs. According to Iwamoto and Chun (2020) , anxiety and depression are the negative emotions that an individual may develop when some source of stress is present. In other words, when social media sources become stress-inducing, there are high chances that anxiety and depression also develop.

Charoensukmongkol (2018) reckoned that the mental health and well-being of the global population can be at a great risk through the uncontrolled massive use of social media. These researchers also showed that social media sources can exert negative affective impacts on teenagers, as they can induce more envy and social comparison. According to Fleck and Johnson-Migalski (2015) , though social media, at first, plays the role of a stress-coping strategy, when individuals continue to see stressful conditions (probably experienced and shared by others in media), they begin to develop stress through the passage of time. Chukwuere and Chukwuere (2017) maintained that social media platforms continue to be the major source of changing mood among general populations. For example, someone might be passively using a social media sphere, and s/he may finally find him/herself with a changed mood depending on the nature of the content faced. Then, this good or bad mood is easily shared with others in a flash through the social media. Finally, as Alahmar (2016) described, social media exposes people especially the young generation to new exciting activities and events that may attract them and keep them engaged in different media contexts for hours just passing their time. It usually leads to reduced productivity, reduced academic achievement, and addiction to constant media use ( Alahmar, 2016 ).

The number of studies on the potential psychological effects of social media on people in general is higher than those selectively addressed here. For further insights into this issue, some other suggested works of research include Chang (2012) , Sriwilai and Charoensukmongkol (2016) , and Zareen et al. (2016) . Now, we move to the studies that more specifically explored the effects of social media on students’ affective states.

Review of the affective influences of social media on students

Vygotsky’s mediational theory (see Fernyhough, 2008 ) can be regarded as a main theoretical background for the support of social media on learners’ affective states. Based on this theory, social media can play the role of a mediational means between learners and the real environment. Learners’ understanding of this environment can be mediated by the image shaped via social media. This image can be either close to or different from the reality. In the case of the former, learners can develop their self-image and self-esteem. In the case of the latter, learners might develop unrealistic expectations of themselves by comparing themselves to others. As it will be reviewed below among the affective variables increased or decreased in students under the influence of the massive use of social media are anxiety, stress, depression, distress, rumination, and self-esteem. These effects have been explored more among school students in the age range of 13–18 than university students (above 18), but some studies were investigated among college students as well. Exemplary works of research on these affective variables are reviewed here.

In a cross-sectional study, O’Dea and Campbell (2011) explored the impact of online interactions of social networks on the psychological distress of adolescent students. These researchers found a negative correlation between the time spent on social networking and mental distress. Dumitrache et al. (2012) explored the relations between depression and the identity associated with the use of the popular social media, the Facebook. This study showed significant associations between depression and the number of identity-related information pieces shared on this social network. Neira and Barber (2014) explored the relationship between students’ social media use and depressed mood at teenage. No significant correlation was found between these two variables. In the same year, Tsitsika et al. (2014) explored the associations between excessive use of social media and internalizing emotions. These researchers found a positive correlation between more than 2-h a day use of social media and anxiety and depression.

Hanprathet et al. (2015) reported a statistically significant positive correlation between addiction to Facebook and depression among about a thousand high school students in wealthy populations of Thailand and warned against this psychological threat. Sampasa-Kanyinga and Lewis (2015) examined the relationship between social media use and psychological distress. These researchers found that the use of social media for more than 2 h a day was correlated with a higher intensity of psychological distress. Banjanin et al. (2015) tested the relationship between too much use of social networking and depression, yet found no statistically significant correlation between these two variables. Frison and Eggermont (2016) examined the relationships between different forms of Facebook use, perceived social support of social media, and male and female students’ depressed mood. These researchers found a positive association between the passive use of the Facebook and depression and also between the active use of the social media and depression. Furthermore, the perceived social support of the social media was found to mediate this association. Besides, gender was found as the other factor to mediate this relationship.

Vernon et al. (2017) explored change in negative investment in social networking in relation to change in depression and externalizing behavior. These researchers found that increased investment in social media predicted higher depression in adolescent students, which was a function of the effect of higher levels of disrupted sleep. Barry et al. (2017) explored the associations between the use of social media by adolescents and their psychosocial adjustment. Social media activity showed to be positively and moderately associated with depression and anxiety. Another investigation was focused on secondary school students in China conducted by Li et al. (2017) . The findings showed a mediating role of insomnia on the significant correlation between depression and addiction to social media. In the same year, Yan et al. (2017) aimed to explore the time spent on social networks and its correlation with anxiety among middle school students. They found a significant positive correlation between more than 2-h use of social networks and the intensity of anxiety.

Also in China, Wang et al. (2018) showed that addiction to social networking sites was correlated positively with depression, and this correlation was mediated by rumination. These researchers also found that this mediating effect was moderated by self-esteem. It means that the effect of addiction on depression was compounded by low self-esteem through rumination. In another work of research, Drouin et al. (2018) showed that though social media is expected to act as a form of social support for the majority of university students, it can adversely affect students’ mental well-being, especially for those who already have high levels of anxiety and depression. In their research, the social media resources were found to be stress-inducing for half of the participants, all university students. The higher education population was also studied by Iwamoto and Chun (2020) . These researchers investigated the emotional effects of social media in higher education and found that the socially supportive role of social media was overshadowed in the long run in university students’ lives and, instead, fed into their perceived depression, anxiety, and stress.

Keles et al. (2020) provided a systematic review of the effect of social media on young and teenage students’ depression, psychological distress, and anxiety. They found that depression acted as the most frequent affective variable measured. The most salient risk factors of psychological distress, anxiety, and depression based on the systematic review were activities such as repeated checking for messages, personal investment, the time spent on social media, and problematic or addictive use. Similarly, Mathewson (2020) investigated the effect of using social media on college students’ mental health. The participants stated the experience of anxiety, depression, and suicidality (thoughts of suicide or attempts to suicide). The findings showed that the types and frequency of using social media and the students’ perceived mental health were significantly correlated with each other.

The body of research on the effect of social media on students’ affective and emotional states has led to mixed results. The existing literature shows that there are some positive and some negative affective impacts. Yet, it seems that the latter is pre-dominant. Mathewson (2020) attributed these divergent positive and negative effects to the different theoretical frameworks adopted in different studies and also the different contexts (different countries with whole different educational systems). According to Fredrickson’s broaden-and-build theory of positive emotions ( Fredrickson, 2001 ), the mental repertoires of learners can be built and broadened by how they feel. For instance, some external stimuli might provoke negative emotions such as anxiety and depression in learners. Having experienced these negative emotions, students might repeatedly check their messages on social media or get addicted to them. As a result, their cognitive repertoire and mental capacity might become limited and they might lose their concentration during their learning process. On the other hand, it should be noted that by feeling positive, learners might take full advantage of the affordances of the social media and; thus, be able to follow their learning goals strategically. This point should be highlighted that the link between the use of social media and affective states is bi-directional. Therefore, strategic use of social media or its addictive use by students can direct them toward either positive experiences like enjoyment or negative ones such as anxiety and depression. Also, these mixed positive and negative effects are similar to the findings of several other relevant studies on general populations’ psychological and emotional health. A number of studies (with general research populations not necessarily students) showed that social networks have facilitated the way of staying in touch with family and friends living far away as well as an increased social support ( Zhang, 2017 ). Given the positive and negative emotional effects of social media, social media can either scaffold the emotional repertoire of students, which can develop positive emotions in learners, or induce negative provokers in them, based on which learners might feel negative emotions such as anxiety and depression. However, admittedly, social media has also generated a domain that encourages the act of comparing lives, and striving for approval; therefore, it establishes and internalizes unrealistic perceptions ( Virden et al., 2014 ; Radovic et al., 2017 ).

It should be mentioned that the susceptibility of affective variables to social media should be interpreted from a dynamic lens. This means that the ecology of the social media can make changes in the emotional experiences of learners. More specifically, students’ affective variables might self-organize into different states under the influence of social media. As for the positive correlation found in many studies between the use of social media and such negative effects as anxiety, depression, and stress, it can be hypothesized that this correlation is induced by the continuous comparison the individual makes and the perception that others are doing better than him/her influenced by the posts that appear on social media. Using social media can play a major role in university students’ psychological well-being than expected. Though most of these studies were correlational, and correlation is not the same as causation, as the studies show that the number of participants experiencing these negative emotions under the influence of social media is significantly high, more extensive research is highly suggested to explore causal effects ( Mathewson, 2020 ).

As the review of exemplary studies showed, some believed that social media increased comparisons that students made between themselves and others. This finding ratifies the relevance of the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ) and Festinger’s (1954) Social Comparison Theory. Concerning the negative effects of social media on students’ psychology, it can be argued that individuals may fail to understand that the content presented in social media is usually changed to only represent the attractive aspects of people’s lives, showing an unrealistic image of things. We can add that this argument also supports the relevance of the Social Comparison Theory and the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ), because social media sets standards that students think they should compare themselves with. A constant observation of how other students or peers are showing their instances of achievement leads to higher self-evaluation ( Stapel and Koomen, 2000 ). It is conjectured that the ubiquitous role of social media in student life establishes unrealistic expectations and promotes continuous comparison as also pinpointed in the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ).

Implications of the study

The use of social media is ever increasing among students, both at school and university, which is partly because of the promises of technological advances in communication services and partly because of the increased use of social networks for educational purposes in recent years after the pandemic. This consistent use of social media is not expected to leave students’ psychological, affective and emotional states untouched. Thus, it is necessary to know how the growing usage of social networks is associated with students’ affective health on different aspects. Therefore, we found it useful to summarize the research findings in recent years in this respect. If those somehow in charge of student affairs in educational settings are aware of the potential positive or negative effects of social media usage on students, they can better understand the complexities of students’ needs and are better capable of meeting them.

Psychological counseling programs can be initiated at schools or universities to check upon the latest state of students’ mental and emotional health influenced by the pervasive use of social media. The counselors can be made aware of the potential adverse effects of social networking and can adapt the content of their inquiries accordingly. Knowledge of the potential reasons for student anxiety, depression, and stress can help school or university counselors to find individualized coping strategies when they diagnose any symptom of distress in students influenced by an excessive use of social networking.

Admittedly, it is neither possible to discard the use of social media in today’s academic life, nor to keep students’ use of social networks fully controlled. Certainly, the educational space in today’s world cannot do without the social media, which has turned into an integral part of everybody’s life. Yet, probably students need to be instructed on how to take advantage of the media and to be the least affected negatively by its occasional superficial and unrepresentative content. Compensatory programs might be needed at schools or universities to encourage students to avoid making unrealistic and impartial comparisons of themselves and the flamboyant images of others displayed on social media. Students can be taught to develop self-appreciation and self-care while continuing to use the media to their benefit.

The teachers’ role as well as the curriculum developers’ role are becoming more important than ever, as they can significantly help to moderate the adverse effects of the pervasive social media use on students’ mental and emotional health. The kind of groupings formed for instructional purposes, for example, in social media can be done with greater care by teachers to make sure that the members of the groups are homogeneous and the tasks and activities shared in the groups are quite relevant and realistic. The teachers cannot always be in a full control of students’ use of social media, and the other fact is that students do not always and only use social media for educational purposes. They spend more time on social media for communicating with friends or strangers or possibly they just passively receive the content produced out of any educational scope just for entertainment. This uncontrolled and unrealistic content may give them a false image of life events and can threaten their mental and emotional health. Thus, teachers can try to make students aware of the potential hazards of investing too much of their time on following pages or people that publish false and misleading information about their personal or social identities. As students, logically expected, spend more time with their teachers than counselors, they may be better and more receptive to the advice given by the former than the latter.

Teachers may not be in full control of their students’ use of social media, but they have always played an active role in motivating or demotivating students to take particular measures in their academic lives. If teachers are informed of the recent research findings about the potential effects of massively using social media on students, they may find ways to reduce students’ distraction or confusion in class due to the excessive or over-reliant use of these networks. Educators may more often be mesmerized by the promises of technology-, computer- and mobile-assisted learning. They may tend to encourage the use of social media hoping to benefit students’ social and interpersonal skills, self-confidence, stress-managing and the like. Yet, they may be unaware of the potential adverse effects on students’ emotional well-being and, thus, may find the review of the recent relevant research findings insightful. Also, teachers can mediate between learners and social media to manipulate the time learners spend on social media. Research has mainly indicated that students’ emotional experiences are mainly dependent on teachers’ pedagogical approach. They should refrain learners from excessive use of, or overreliance on, social media. Raising learners’ awareness of this fact that individuals should develop their own path of development for learning, and not build their development based on unrealistic comparison of their competences with those of others, can help them consider positive values for their activities on social media and, thus, experience positive emotions.

At higher education, students’ needs are more life-like. For example, their employment-seeking spirits might lead them to create accounts in many social networks, hoping for a better future. However, membership in many of these networks may end in the mere waste of the time that could otherwise be spent on actual on-campus cooperative projects. Universities can provide more on-campus resources both for research and work experience purposes from which the students can benefit more than the cyberspace that can be tricky on many occasions. Two main theories underlying some negative emotions like boredom and anxiety are over-stimulation and under-stimulation. Thus, what learners feel out of their involvement in social media might be directed toward negative emotions due to the stimulating environment of social media. This stimulating environment makes learners rely too much, and spend too much time, on social media or use them obsessively. As a result, they might feel anxious or depressed. Given the ubiquity of social media, these negative emotions can be replaced with positive emotions if learners become aware of the psychological effects of social media. Regarding the affordances of social media for learners, they can take advantage of the potential affordances of these media such as improving their literacy, broadening their communication skills, or enhancing their distance learning opportunities.

A review of the research findings on the relationship between social media and students’ affective traits revealed both positive and negative findings. Yet, the instances of the latter were more salient and the negative psychological symptoms such as depression, anxiety, and stress have been far from negligible. These findings were discussed in relation to some more relevant theories such as the social comparison theory, which predicted that most of the potential issues with the young generation’s excessive use of social media were induced by the unfair comparisons they made between their own lives and the unrealistic portrayal of others’ on social media. Teachers, education policymakers, curriculum developers, and all those in charge of the student affairs at schools and universities should be made aware of the psychological effects of the pervasive use of social media on students, and the potential threats.

It should be reminded that the alleged socially supportive and communicative promises of the prevalent use of social networking in student life might not be fully realized in practice. Students may lose self-appreciation and gratitude when they compare their current state of life with the snapshots of others’ or peers’. A depressed or stressed-out mood can follow. Students at schools or universities need to learn self-worth to resist the adverse effects of the superficial support they receive from social media. Along this way, they should be assisted by the family and those in charge at schools or universities, most importantly the teachers. As already suggested, counseling programs might help with raising students’ awareness of the potential psychological threats of social media to their health. Considering the ubiquity of social media in everybody’ life including student life worldwide, it seems that more coping and compensatory strategies should be contrived to moderate the adverse psychological effects of the pervasive use of social media on students. Also, the affective influences of social media should not be generalized but they need to be interpreted from an ecological or contextual perspective. This means that learners might have different emotions at different times or different contexts while being involved in social media. More specifically, given the stative approach to learners’ emotions, what learners emotionally experience in their application of social media can be bound to their intra-personal and interpersonal experiences. This means that the same learner at different time points might go through different emotions Also, learners’ emotional states as a result of their engagement in social media cannot be necessarily generalized to all learners in a class.

As the majority of studies on the psychological effects of social media on student life have been conducted on school students than in higher education, it seems it is too soon to make any conclusive remark on this population exclusively. Probably, in future, further studies of the psychological complexities of students at higher education and a better knowledge of their needs can pave the way for making more insightful conclusions about the effects of social media on their affective states.

Suggestions for further research

The majority of studies on the potential effects of social media usage on students’ psychological well-being are either quantitative or qualitative in type, each with many limitations. Presumably, mixed approaches in near future can better provide a comprehensive assessment of these potential associations. Moreover, most studies on this topic have been cross-sectional in type. There is a significant dearth of longitudinal investigation on the effect of social media on developing positive or negative emotions in students. This seems to be essential as different affective factors such as anxiety, stress, self-esteem, and the like have a developmental nature. Traditional research methods with single-shot designs for data collection fail to capture the nuances of changes in these affective variables. It can be expected that more longitudinal studies in future can show how the continuous use of social media can affect the fluctuations of any of these affective variables during the different academic courses students pass at school or university.

As already raised in some works of research reviewed, the different patterns of impacts of social media on student life depend largely on the educational context. Thus, the same research designs with the same academic grade students and even the same age groups can lead to different findings concerning the effects of social media on student psychology in different countries. In other words, the potential positive and negative effects of popular social media like Facebook, Snapchat, Twitter, etc., on students’ affective conditions can differ across different educational settings in different host countries. Thus, significantly more research is needed in different contexts and cultures to compare the results.

There is also a need for further research on the higher education students and how their affective conditions are positively and negatively affected by the prevalent use of social media. University students’ psychological needs might be different from other academic grades and, thus, the patterns of changes that the overall use of social networking can create in their emotions can be also different. Their main reasons for using social media might be different from school students as well, which need to be investigated more thoroughly. The sorts of interventions needed to moderate the potential negative effects of social networking on them can be different too, all requiring a new line of research in education domain.

Finally, there are hopes that considering the ever-increasing popularity of social networking in education, the potential psychological effects of social media on teachers be explored as well. Though teacher psychology has only recently been considered for research, the literature has provided profound insights into teachers developing stress, motivation, self-esteem, and many other emotions. In today’s world driven by global communications in the cyberspace, teachers like everyone else are affecting and being affected by social networking. The comparison theory can hold true for teachers too. Thus, similar threats (of social media) to self-esteem and self-worth can be there for teachers too besides students, which are worth investigating qualitatively and quantitatively.

Probably a new line of research can be initiated to explore the co-development of teacher and learner psychological traits under the influence of social media use in longitudinal studies. These will certainly entail sophisticated research methods to be capable of unraveling the nuances of variation in these traits and their mutual effects, for example, stress, motivation, and self-esteem. If these are incorporated within mixed-approach works of research, more comprehensive and better insightful findings can be expected to emerge. Correlational studies need to be followed by causal studies in educational settings. As many conditions of the educational settings do not allow for having control groups or randomization, probably, experimental studies do not help with this. Innovative research methods, case studies or else, can be used to further explore the causal relations among the different features of social media use and the development of different affective variables in teachers or learners. Examples of such innovative research methods can be process tracing, qualitative comparative analysis, and longitudinal latent factor modeling (for a more comprehensive view, see Hiver and Al-Hoorie, 2019 ).

Author contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This study was sponsored by Wuxi Philosophy and Social Sciences bidding project—“Special Project for Safeguarding the Rights and Interests of Workers in the New Form of Employment” (Grant No. WXSK22-GH-13). This study was sponsored by the Key Project of Party Building and Ideological and Political Education Research of Nanjing University of Posts and Telecommunications—“Research on the Guidance and Countermeasures of Network Public Opinion in Colleges and Universities in the Modern Times” (Grant No. XC 2021002).

Conflict of interest

Author XX was employed by China Mobile Group Jiangsu Co., Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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1 Introduction

1.1 Terminology

1.2 Research challenges

1.3 Social media research and applications

1.4 Social media overview

2 Social media methodology and critique

2.1 Methodology

2.1.2 Analytics

2.1.3 Facilities

2.2 Critique

2.2.2 Analytics

2.2.3 Facilities

3 Social media data

3.1 Types of data

3.2 Text data formats

4 Social media providers

4.1 Open-source databases

4.2 Data access via tools

4.2.1 Freely accessible sources

4.2.2 Commercial sources

4.3 Data feed access via APIs

4.3.1 Wiki media

4.3.2 Social networking media

4.3.2.1 Twitter

4.3.2.2 Facebook

4.3.3 RSS feeds

4.3.4 Blogs, news groups and chat services

4.3.5 News feeds

4.3.6 Geospatial feeds

5 Text cleaning, tagging and storing

5.1 Cleansing data