research instrument of social media

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Research Article

Social impact in social media: A new method to evaluate the social impact of research

Roles Investigation, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Journalism and Communication Studies, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Psychology and Sociology, Universidad de Zaragoza, Zaragoza, Spain

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliation Department of Sociology, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Sociology, Universitat de Barcelona (UB), Barcelona, Spain

• Cristina M. Pulido, 
• Gisela Redondo-Sama, 
• Teresa Sordé-Martí, 
• Ramon Flecha

• Published: August 29, 2018
• https://doi.org/10.1371/journal.pone.0203117
• Reader Comments

The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.

Citation: Pulido CM, Redondo-Sama G, Sordé-Martí T, Flecha R (2018) Social impact in social media: A new method to evaluate the social impact of research. PLoS ONE 13(8): e0203117. https://doi.org/10.1371/journal.pone.0203117

Editor: Sergi Lozano, Institut Català de Paleoecologia Humana i Evolució Social (IPHES), SPAIN

Received: November 8, 2017; Accepted: August 15, 2018; Published: August 29, 2018

Copyright: © 2018 Pulido et al. 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: All relevant data are within the paper and its Supporting Information files.

Funding: The research leading to these results has received funding from the 7th Framework Programme of the European Commission under the Grant Agreement n° 613202 P.I. Ramon Flecha, https://ec.europa.eu/research/fp7/index_en.cfm . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

Introduction

The social impact of research is at the core of some of the debates influencing how scientists develop their studies and how useful results for citizens and societies may be obtained. Concrete strategies to achieve social impact in particular research projects are related to a broader understanding of the role of science in contemporary society. There is a need to explore dialogues between science and society not only to communicate and disseminate science but also to achieve social improvements generated by science. Thus, the social impact of research emerges as an increasing concern within the scientific community [ 1 ]. As Bornmann [ 2 ] said, the assessment of this type of impact is badly needed and is more difficult than the measurement of scientific impact; for this reason, it is urgent to advance in the methodologies and approaches to measuring the social impact of research.

Several authors have approached the conceptualization of social impact, observing a lack of generally accepted conceptual and instrumental frameworks [ 3 ]. It is common to find a wide range of topics included in the contributions about social impact. In their analysis of the policies affecting land use, Hemling et al. [ 4 ] considered various domains in social impact, for instance, agricultural employment or health risk. Moving to the field of flora and fauna, Wilder and Walpole [ 5 ] studied the social impact of conservation projects, focusing on qualitative stories that provided information about changes in attitudes, behaviour, wellbeing and livelihoods. In an extensive study by Godin and Dore [ 6 ], the authors provided an overview and framework for the assessment of the contribution of science to society. They identified indicators of the impact of science, mentioning some of the most relevant weaknesses and developing a typology of impact that includes eleven dimensions, with one of them being the impact on society. The subdimensions of the impact of science on society focus on individuals (wellbeing and quality of life, social implication and practices) and organizations (speeches, interventions and actions). For the authors, social impact “refers to the impact knowledge has on welfare, and on the behaviours, practices and activities of people and groups” (p. 7).

In addition, the terms “social impact” and “societal impact” are sometimes used interchangeably. For instance, Bornmann [ 2 ] said that due to the difficulty of distinguishing social benefits from the superior term of societal benefits, “in much literature the term ‘social impact’ is used instead of ‘societal impact’”(p. 218). However, in other cases, the distinction is made [ 3 ], as in the present research. Similar to the definition used by the European Commission [ 7 ], social impact is used to refer to economic impact, societal impact, environmental impact and, additionally, human rights impact. Therefore, we use the term social impact as the broader concept that includes social improvements in all the above mentioned areas obtained from the transference of research results and representing positive steps towards the fulfilment of those officially defined social goals, including the UN Sustainable Development Goals, the EU 2020 Agenda, or similar official targets. For instance, the Europe 2020 strategy defines five priority targets with concrete indicators (employment, research and development, climate change and energy, education and poverty and social exclusion) [ 8 ], and we consider the targets addressed by objectives defined in the specific call that funds the research project.

This understanding of the social impact of research is connected to the creation of the Social Impact Open Repository (SIOR), which constitutes the first open repository worldwide that displays, cites and stores the social impact of research results [ 9 ]. The SIOR has linked to ORCID and Wikipedia to allow the synergies of spreading information about the social impact of research through diverse channels and audiences. It is relevant to mention that currently, SIOR includes evidence of real social impact, which implies that the research results have led to actual improvements in society. However, it is common to find evidence of potential social impact in research projects. The potential social impact implies that in the development of the research, there has been some evidence of the effectiveness of the research results in terms of social impact, but the results have not yet been transferred.

Additionally, a common confusion is found among the uses of dissemination, transference (policy impact) and social impact. While dissemination means to disseminate the knowledge created by research to citizens, companies and institutions, transference refers to the use of this knowledge by these different actors (or others), and finally, as already mentioned, social impact refers to the actual improvements resulting from the use of this knowledge in relation to the goals motivating the research project (such as the United Nations Sustainable Development Goals). In the present research [ 3 ], it is argued that “social impact can be understood as the culmination of the prior three stages of the research” (p.3). Therefore, this study builds on previous contributions measuring the dissemination and transference of research and goes beyond to propose a novel methodological approach to track social impact evidences.

In fact, the contribution that we develop in this article is based on the creation of a new method to evaluate the evidence of social impact shared in social media. The evaluation proposed is to measure the social impact coverage ratio (SICOR), focusing on the presence of evidence of social impact shared in social media. Then, the article first presents some of the contributions from the literature review focused on the research on social media as a source for obtaining key data for monitoring or evaluating different research purposes. Second, the SISM (social impact through social media) methodology[ 10 ] developed is introduced in detail. This methodology identifies quantitative and qualitative evidence of the social impact of the research shared on social media, specifically on Twitter and Facebook, and defines the SICOR, the social impact coverage ratio. Next, the results are discussed, and lastly, the main conclusions and further steps are presented.

Literature review

Social media research includes the analysis of citizens’ voices on a wide range of topics [ 11 ]. According to quantitative data from April 2017 published by Statista [ 12 ], Twitter and Facebook are included in the top ten leading social networks worldwide, as ranked by the number of active users. Facebook is at the top of the list, with 1,968 million active users, and Twitter ranks 10 th , with 319 million active users. Between them are the following social networks: WhatsApp, YouTube, Facebook Messenger, WeChat, QQ, Instagram,Qzone and Tumblr. If we look at altmetrics, the tracking of social networks for mentions of research outputs includes Facebook, Twitter, Google+,LinkedIn, Sina Weibo and Pinterest. The social networks common to both sources are Facebook and Twitter. These are also popular platforms that have a relevant coverage of scientific content and easy access to data, and therefore, the research projects selected here for application of the SISM methodology were chosen on these platforms.

Chew and Eysenbach [ 13 ] studied the presence of selected keywords in Twitter related to public health issues, particularly during the 2009 H1N1 pandemic, identifying the potential for health authorities to use social media to respond to the concerns and needs of society. Crooks et al.[ 14 ] investigated Twitter activity in the context of a 5.8 magnitude earthquake in 2011 on the East Coast of the United States, concluding that social media content can be useful for event monitoring and can complement other sources of data to improve the understanding of people’s responses to such events. Conversations among young Canadians posted on Facebook and analysed by Martinello and Donelle [ 15 ] revealed housing and transportation as main environmental concerns, and the project FoodRisc examined the role of social media to illustrate consumers’ quick responses during food crisis situations [ 16 ]. These types of contributions illustrate that social media research implies the understanding of citizens’ concerns in different fields, including in relation to science.

Research on the synergies between science and citizens has increased over the years, according to Fresco [ 17 ], and there is a growing interest among researchers and funding agencies in how to facilitate communication channels to spread scientific results. For instance, in 1998, Lubchenco [ 18 ] advocated for a social contract that “represents a commitment on the part of all scientists to devote their energies and talents to the most pressing problems of the day, in proportion to their importance, in exchange for public funding”(p.491).

In this framework, the recent debates on how to increase the impact of research have acquired relevance in all fields of knowledge, and major developments address the methods for measuring it. As highlighted by Feng Xia et al. [ 19 ], social media constitute an emerging approach to evaluating the impact of scholarly publications, and it is relevant to consider the influence of the journal, discipline, publication year and user type. The authors revealed that people’s concerns differ by discipline and observed more interest in papers related to everyday life, biology, and earth and environmental sciences. In the field of biomedical sciences, Haustein et al. [ 20 ] analysed the dissemination of journal articles on Twitter to explore the correlations between tweets and citations and proposed a framework to evaluate social media-based metrics. In fact, different studies address the relationship between the presence of articles on social networks and citations [ 21 ]. Bornmann [ 22 ] conducted a case study using a sample of 1,082 PLOS journal articles recommended in F1000 to explore the usefulness of altmetrics for measuring the broader impact of research. The author presents evidence about Facebook and Twitter as social networks that may indicate which papers in the biomedical sciences can be of interest to broader audiences, not just to specialists in the area. One aspect of particular interest resulting from this contribution is the potential to use altmetrics to measure the broader impacts of research, including the societal impact. However, most of the studies investigating social or societal impact lack a conceptualization underlying its measurement.

To the best of our knowledge, the assessment of social impact in social media (SISM) has developed according to this gap. At the core of this study, we present and discuss the results obtained through the application of the SICOR (social impact coverage ratio) with examples of evidence of social impact shared in social media, particularly on Twitter and Facebook, and the implications for further research.

Following these previous contributions, our research questions were as follows: Is there evidence of social impact of research shared by citizens in social media? If so, is there quantitative or qualitative evidence? How can social media contribute to identifying the social impact of research?

Methods and data presentation

A group of new methodologies related to the analysis of online data has recently emerged. One of these emerging methodologies is social media analytics [ 23 ], which was initially used most in the marketing research field but also came to be used in other domains due to the multiple possibilities opened up by the availability and richness of the data for different research purposes. Likewise, the concern of how to evaluate the social impact of research as well as the development of methodologies for addressing this concern has occupied central attention. The development of SISM (Social Impact in Social Media) and the application of the SICOR (Social Impact Coverage Ratio) is a contribution to advancement in the evaluation of the social impact of research through the analysis of the social media selected (in this case, Twitter and Facebook). Thus, SISM is novel in both social media analytics and among the methodologies used to evaluate the social impact of research. This development has been made under IMPACT-EV, a research project funded under the Framework Program FP7 of the Directorate-General for Research and Innovation of the European Commission. The main difference from other methodologies for measuring the social impact of research is the disentanglement between dissemination and social impact. While altmetrics is aimed at measuring research results disseminated beyond academic and specialized spheres, SISM contribute to advancing this measurement by shedding light on to what extent evidence of the social impact of research is found in social media data. This involves the need to differentiate between tweets or Facebook posts (Fb/posts) used to disseminate research findings from those used to share the social impact of research. We focus on the latter, investigating whether there is evidence of social impact, including both potential and real social impact. In fact, the question is whether research contributes and/or has the potential to contribute to improve the society or living conditions considering one of these goals defined. What is the evidence? Next, we detail the application of the methodology.

Data collection

To develop this study, the first step was to select research projects with social media data to be analysed. The selection of research projects for application of the SISM methodology was performed according to three criteria.

Criteria 1. Selection of success projects in FP7. The projects were success stories of the 7 th Framework Programme (FP7) highlighted by the European Commission [ 24 ] in the fields of knowledge of medicine, public health, biology and genomics. The FP7 published calls for project proposals from 2007 to 2013. This implies that most of the projects funded in the last period of the FP7 (2012 and 2013) are finalized or in the last phase of implementation.

Criteria 2. Period of implementation. We selected projects in the 2012–2013 period because they combine recent research results with higher possibilities of having Twitter and Facebook accounts compared with projects of previous years, as the presence of social accounts in research increased over this period.

Criteria 3. Twitter and Facebook accounts. It was crucial that the selected projects had active Twitter and Facebook accounts.

Table 1 summarizes the criteria and the final number of projects identified. As shown, 10 projects met the defined criteria. Projects in medical research and public health had higher presence.

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https://doi.org/10.1371/journal.pone.0203117.t001

After the selection of projects, we defined the timeframe of social media data extraction on Twitter and Facebook from the starting date of the project until the day of the search, as presented in Table 2 .

https://doi.org/10.1371/journal.pone.0203117.t002

The second step was to define the search strategies for extracting social media data related to the research projects selected. In this line, we defined three search strategies.

Strategy 1. To extract messages published on the Twitter account and the Facebook page of the selected projects. We listed the Twitter accounts and Facebook pages related to each project in order to look at the available information. In this case, it is important to clarify that the tweets published under the corresponding Twitter project account are original tweets or retweets made from this account. It is relevant to mention that in one case, the Twitter account and Facebook page were linked to the website of the research group leading the project. In this case, we selected tweets and Facebook posts related to the project. For instance, in the case of the Twitter account, the research group created a specific hashtag to publish messages related to the project; therefore, we selected only the tweets published under this hashtag. In the analysis, we prioritized the analysis of the tweets and Facebook posts that received some type of interaction (likes, retweets or shares) because such interaction is a proxy for citizens’ interest. In doing so, we used the R program and NVivoto extract the data and proceed with the analysis. Once we obtained the data from Twitter and Facebook, we were able to have an overview of the information to be further analysed, as shown in Table 3 .

https://doi.org/10.1371/journal.pone.0203117.t003

We focused the second and third strategies on Twitter data. In both strategies, we extracted Twitter data directly from the Twitter Advanced Search tool, as the API connected to NVivo and the R program covers only a specific period of time limited to 7/9 days. Therefore, the use of the Twitter Advanced Search tool made it possible to obtain historic data without a period limitation. We downloaded the results in PDF and then uploaded them to NVivo.

Strategy 2. To use the project acronym combined with other keywords, such as FP7 or EU. This strategy made it possible to obtain tweets mentioning the project. Table 4 presents the number of tweets obtained with this strategy.

https://doi.org/10.1371/journal.pone.0203117.t004

Strategy 3. To use searchable research results of projects to obtain Twitter data. We defined a list of research results, one for each project, and converted them into keywords. We selected one searchable keyword for each project from its website or other relevant sources, for instance, the brief presentations prepared by the European Commission and published in CORDIS. Once we had the searchable research results, we used the Twitter Advanced Search tool to obtain tweets, as presented in Table 5 .

https://doi.org/10.1371/journal.pone.0203117.t005

The sum of the data obtained from these three strategies allowed us to obtain a total of 3,425 tweets and 1,925 posts on public Facebook pages. Table 6 presents a summary of the results.

https://doi.org/10.1371/journal.pone.0203117.t006

We imported the data obtained from the three search strategies into NVivo to analyse. Next, we select tweets and Facebook posts providing linkages with quantitative or qualitative evidence of social impact, and we complied with the terms of service for the social media from which the data were collected. By quantitative and qualitative evidence, we mean data or information that shows how the implementation of research results has led to improvements towards the fulfilment of the objectives defined in the EU2020 strategy of the European Commission or other official targets. For instance, in the case of quantitative evidence, we searched tweets and Facebook posts providing linkages with quantitative information about improvements obtained through the implementation of the research results of the project. In relation to qualitative evidence, for example, we searched for testimonies that show a positive evaluation of the improvement due to the implementation of research results. In relation to this step, it is important to highlight that social media users are intermediaries making visible evidence of social impact. Users often share evidence, sometimes sharing a link to an external resource (e.g., a video, an official report, a scientific article, news published on media). We identified evidence of social impact in these sources.

Data analysis

γ i is the total number of messages obtained about project i with evidence of social impact on social media platforms (Twitter, Facebook, Instagram, etc.);

T i is the total number of messages from project i on social media platforms (Twitter, Facebook, Instagram, etc.); and

n is the number of projects selected.

Analytical categories and codebook

The researchers who carried out the analysis of the social media data collected are specialists in the social impact of research and research on social media. Before conducting the full analysis, two aspects were guaranteed. First, how to identify evidence of social impact relating to the targets defined by the EU2020 strategy or to specific goals defined by the call addressed was clarified. Second, we held a pilot to test the methodology with one research project that we know has led to considerable social impact, which allowed us to clarify whether or not it was possible to detect evidence of social impact shared in social media. Once the pilot showed positive results, the next step was to extend the analysis to another set of projects and finally to the whole sample. The construction of the analytical categories was defined a priori, revised accordingly and lastly applied to the full sample.

Different observations should be made. First, in this previous analysis, we found that the tweets and Facebook users play a key role as “intermediaries,” serving as bridges between the larger public and the evidence of social impact. Social media users usually share a quote or paragraph introducing evidence of social impact and/or link to an external resource, for instance, a video, official report, scientific article, news story published on media, etc., where evidence of the social impact is available. This fact has implications for our study, as our unit of analysis is all the information included in the tweets or Facebook posts. This means that our analysis reaches the external resources linked to find evidence of social impact, and for this reason, we defined tweets or Facebook posts providing linkages with information about social impact.

Second, the other important aspect is the analysis of the users’ profile descriptions, which requires much more development in future research given the existing limitations. For instance, some profiles are users’ restricted due to privacy reasons, so the information is not available; other accounts have only the name of the user with no description of their profile available. Therefore, we gave priority to the identification of evidence of social impact including whether a post obtained interaction (retweets, likes or shares) or was published on accounts other than that of the research project itself. In the case of the profile analysis, we added only an exploratory preliminary result because this requires further development. Considering all these previous details, the codebook (see Table 7 ) that we present as follows is a result of this previous research.

https://doi.org/10.1371/journal.pone.0203117.t007

How to analyse Twitter and Facebook data

To illustrate how we analysed data from Twitter and Facebook, we provide one example of each type of evidence of social impact defined, considering both real and potential social impact, with the type of interaction obtained and the profiles of those who have interacted.

QUANESISM. Tweet by ZeroHunger Challenge @ZeroHunger published on 3 May 2016. Text: How re-using food waste for animal feed cuts carbon emissions.-NOSHAN project hubs.ly/H02SmrP0. 7 retweets and 5 likes.

The unit of analysis is all the content of the tweet, including the external link. If we limited our analysis to the tweet itself, it would not be evidence. Examining the external link is necessary to find whether there is evidence of social impact. The aim of this project was to investigate the process and technologies needed to use food waste for feed production at low cost, with low energy consumption and with a maximal evaluation of the starting wastes. This tweet provides a link to news published in the PHYS.org portal [ 25 ], which specializes in science news. The news story includes an interview with the main researcher that provides the following quotation with quantitative evidence:

'Our results demonstrated that with a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions were reduced by 0.3 kg compared to a non-food waste diet,' explains Montse Jorba, NOSHAN project coordinator. 'If 1 percent of total chicken broiler feed in Europe was switched to the 10 percent NOSHAN mix diet, the total amount of CO2 emissions avoided would be 0.62 million tons each year.'[ 25 ]

This quantitative evidence “a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions carbon dioxide emissions were reduced by 0.3 kg to a non-food waste diet” is linked directly with the Europe 2020 target of Climate Change & Energy, specifically with the target of reducing greenhouse gas emissions by 20% compared to the levels in 1990 [ 8 ]. The illustrative extrapolation the coordinator mentioned in the news is also an example of quantitative evidence, although is an extrapolation based on the specific research result.

This tweet was captured by the Acronym search strategy. It is a message tweeted by an account that is not related to the research project. The twitter account is that of the Zero Hunger Challenge movement, which supports the goals of the UN. The interaction obtained is 7 retweets and 5 likes. Regarding the profiles of those who retweeted and clicked “like”, there were activists, a journalist, an eco-friendly citizen, a global news service, restricted profiles (no information is available on those who have retweeted) and one account with no information in its profile.

The following example illustrates the analysis of QUALESISM: Tweet by @eurofitFP7 published on4 October 2016. Text: See our great new EuroFIT video on youtube! https://t.co/TocQwMiW3c 9 retweets and 5 likes.

The aim of this project is to improve health through the implementation of two novel technologies to achieve a healthier lifestyle. The tweet provides a link to a video on YouTube on the project’s results. In this video, we found qualitative evidence from people who tested the EuroFit programme; there are quotes from men who said that they have experienced improved health results using this method and that they are more aware of how to manage their health:

One end-user said: I have really amazing results from the start, because I managed to change a lot of things in my life. And other one: I was more conscious of what I ate, I was more conscious of taking more steps throughout the day and also standing up a little more. [ 26 ]

The research applies the well researched scientific evidence to the management of health issues in daily life. The video presents the research but also includes a section where end-users talk about the health improvements they experienced. The quotes extracted are some examples of the testimonies collected. All agree that they have improved their health and learned healthy habits for their daily lives. These are examples of qualitative evidence linked with the target of the call HEALTH.2013.3.3–1—Social innovation for health promotion [ 27 ] that has the objectives of reducing sedentary habits in the population and promoting healthy habits. This research contributes to this target, as we see in the video testimonies. Regarding the interaction obtained, this tweet achieved 9 retweets and 5 likes. In this case, the profiles of the interacting citizens show involvement in sport issues, including sport trainers, sport enthusiasts and some researchers.

To summarize the analysis, in Table 8 below, we provide a summary with examples illustrating the evidence found.

https://doi.org/10.1371/journal.pone.0203117.t008

Quantitative evidence of social impact in social media

There is a greater presence of tweets/Fb posts with quantitative evidence (14) than with qualitative evidence (9) in the total number of tweets/Fb posts identified with evidence of social impact. Most of the tweets/Fb posts with quantitative evidence of social impact are from scientific articles published in peer-reviewed international journals and show potential social impact. In Table 8 , we introduce 3 examples of this type of tweets/Fb posts with quantitative evidence:

The first tweet with quantitative social impact selected is from project 7. The aim of this project was to provide high-quality scientific evidence for preventing vitamin D deficiency in European citizens. The tweet highlighted the main contribution of the published study, that is, “Weekly consumption of 7 vitamin D-enhanced eggs has an important impact on winter vitamin D status in adults” [ 28 ]. The quantitative evidence shared in social media was extracted from a news publication in a blog on health news. This blog collects scientific articles of research results. In this case, the blog disseminated the research result focused on how vitamin D-enhanced eggs improve vitamin D deficiency in wintertime, with the published results obtained by the research team of the project selected. The quantitative evidence illustrates that the group of adults who consumed vitamin D-enhanced eggs did not suffer from vitamin D deficiency, as opposed to the control group, which showed a significant decrease in vitamin D over the winter. The specific evidence is the following extracted from the article [ 28 ]:

With the use of a within-group analysis, it was shown that, although serum 25(OH) D in the control group significantly decreased over winter (mean ± SD: -6.4 ± 6.7 nmol/L; P = 0.001), there was no change in the 2 groups who consumed vitamin D-enhanced eggs (P>0.1 for both. (p. 629)

This evidence contributes to achievement of the target defined in the call addressed that is KBBE.2013.2.2–03—Food-based solutions for the eradication of vitamin D deficiency and health promotion throughout the life cycle [ 29 ]. The quantitative evidence shows how the consumption of vitamin D-enhanced eggs reduces vitamin D deficiency.

The second example of this table corresponds to the example of quantitative evidence of social impact provided in the previous section.

The third example is a Facebook post from project 3 that is also tweeted. Therefore, this evidence was published in both social media sources analysed. The aim of this project was to measure a range of chemical and physical environmental hazards in food, consumer products, water, air, noise, and the built environment in the pre- and postnatal early-life periods. This Facebook post and tweet links directly to a scientific article [ 30 ] that shows the precision of the spectroscopic platform:

Using 1H NMR spectroscopy we characterized short-term variability in urinary metabolites measured from 20 children aged 8–9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%) . (p.1)

This evidence is linked to the target defined in the call “ENV.2012.6.4–3—Integrating environmental and health data to advance knowledge of the role of environment in human health and well-being in support of a European exposome initiative” [ 31 ]. The evidence provided shows how the project’s results have contributed to building technology for improving the data collection to advance in the knowledge of the role of the environment in human health, especially in early life. The interaction obtained is one retweet from a citizen from Nigeria interested in health issues, according to the information available in his profile.

Qualitative evidence of social impact in social media

We found qualitative evidence of the social impact of different projects, as shown in Table 9 . Similarly to the quantitative evidence, the qualitative cases also demonstrate potential social impact. The three examples provided have in common that they are tweets or Facebook posts that link to videos where the end users of the research project explain their improvements once they have implemented the research results.

https://doi.org/10.1371/journal.pone.0203117.t009

The first tweet with qualitative evidence selected is from project 4. The aim of this project is to produce a system that helps in the prevention of obesity and eating disorders, targeting young people and adults [ 32 ]. The twitter account that published this tweet is that of the Future and Emerging Technologies Programme of the European Commission, and a link to a Euronews video is provided. This video shows how the patients using the technology developed in the research achieved control of their eating disorders, through the testimonies of patients commenting on the positive results they have obtained. These testimonies are included in the news article that complements the video. An example of these testimonies is as follows:

Pierre Vial has lost 43 kilos over the past nine and a half months. He and other patients at the eating disorder clinic explain the effects obesity and anorexia have had on their lives. Another patient, Karin Borell, still has some months to go at the clinic but, after decades of battling anorexia, is beginning to be able to visualise life without the illness: “On a good day I see myself living a normal life without an eating disorder, without problems with food. That’s really all I wish right now”.[ 32 ]

This qualitative evidence shows how the research results contribute to the achievement of the target goals of the call addressed:“ICT-2013.5.1—Personalised health, active ageing, and independent living”. [ 33 ] In this case, the results are robust, particularly for people suffering chronic diseases and desiring to improve their health; people who have applied the research findings are improving their eating disorders and better managing their health. The value of this evidence is the inclusion of the patients’ voices stating the impact of the research results on their health.

The second example is a Facebook post from project 9, which provides a link to a Euronews video. The aim of this project is to bring some tools from the lab to the farm in order to guarantee a better management of the farm and animal welfare. In this video [ 34 ], there are quotes from farmers using the new system developed through the research results of the project. These quotes show how use of the new system is improving the management of the farm and the health of the animals; some examples are provided:

Cameras and microphones help me detect in real time when the animals are stressed for whatever reason,” explained farmer Twan Colberts. “So I can find solutions faster and in more efficient ways, without me being constantly here, checking each animal.”

This evidence shows how the research results contribute to addressing the objectives specified in the call “KBBE.2012.1.1–02—Animal and farm-centric approach to precision livestock farming in Europe” [ 29 ], particularly, to improve the precision of livestock farming in Europe. The interaction obtained is composed of6 likes and 1 share. The profiles are diverse, but some of them do not disclose personal information; others have not added a profile description, and only their name and photo are available.

Interrater reliability (kappa)

The analysis of tweets and Facebook posts providing linkages with information about social impact was conducted following a content analysis method in which reliability was based on a peer review process. This sample is composed of 3,425 tweets and 1,925 Fb/posts. Each tweet and Facebook post was analysed to identify whether or not it contains evidence of social impact. Each researcher has the codebook a priori. We used interrater reliability in examining the agreement between the two raters on the assignment of the categories defined through Cohen’s kappa. We used SPSS to calculate this coefficient. We exported an excel sheet with the sample coded by the two researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact) to SPSS. The cases where agreement was not achieved were not considered as containing evidence of social impact. The result obtained is 0.979; considering the interpretation of this number according to Landis & Koch [ 35 ], our level of agreement is almost perfect, and thus, our analysis is reliable. To sum up the data analysis, the description of the steps followed is explained:

Step 1. Data analysis I. We included all data collected in an excel sheet to proceed with the analysis. Prior to the analysis, researchers read the codebook to keep in mind the information that should be identified.

Step 2. Each researcher involved reviewed case by case the tweets and Facebook posts to identify whether they provide links with evidence of social impact or not. If the researcher considers there to be evidence of social impact, he or she introduces the value of 1into the column, and if not, the value of 0.

Step 3. Once all the researchers have finished this step, the next step is to export the excel sheet to SPSS to extract the kappa coefficient.

Step 4. Data Analysis II. The following step was to analyse case by case the tweets and Facebook posts identified as providing linkages with information of social impact and classify them as quantitative or qualitative evidence of social impact.

Step 5. The interaction received was analysed because this determines to which extent this evidence of social impact has captured the attention of citizens (in the form of how many likes, shares, or retweets the post has).

Step 6. Finally, if available, the profile descriptions of the citizens interacting through retweeting or sharing the Facebook post were considered.

Step 7. SICOR was calculated. It could be applied to the complete sample (all data projects) or to each project, as we will see in the next section.

The total number of tweets and Fb/posts collected from the 10 projects is 5,350. After the content analysis, we identified 23 tweets and Facebook posts providing linkages to information about social impact. To respond to the research question, which considered whether there is evidence of social impact shared by citizens in social media, the answer was affirmative, although the coverage ratio is low. Both Twitter and Facebook users retweeted or shared evidence of social impact, and therefore, these two social media networks are valid sources for expanding knowledge on the assessment of social impact. Table 10 shows the social impact coverage ratio in relation to the total number of messages analysed.

https://doi.org/10.1371/journal.pone.0203117.t010

The analysis of each of the projects selected revealed some results to consider. Of the 10 projects, 7 had evidence, but those projects did not necessarily have more Tweets and Facebook posts. In fact, some projects with fewer than 70 tweets and 50 Facebook posts have more evidence of social impact than other projects with more than 400 tweets and 400 Facebook posts. This result indicates that the number of tweets and Facebook posts does not determine the existence of evidence of social impact in social media. For example, project 2 has 403 tweets and 423 Facebooks posts, but it has no evidence of social impact on social media. In contrast, project 9 has 62 tweets, 43 Facebook posts, and 2 pieces of evidence of social impact in social media, as shown in Table 11 .

https://doi.org/10.1371/journal.pone.0203117.t011

The ratio of tweets/Fb posts to evidence is 0.43%, and it differs depending on the project, as shown below in Table 12 . There is one project (P7) with a ratio of 4.98%, which is a social impact coverage ratio higher than that of the other projects. Next, a group of projects (P3, P9, P10) has a social impact coverage ratio between 1.41% and 2,99%.The next slot has three projects (P1, P4, P5), with a ratio between 0.13% and 0.46%. Finally, there are three projects (P2, P6, P8) without any tweets/Fb posts evidence of social impact.

https://doi.org/10.1371/journal.pone.0203117.t012

Considering the three strategies for obtaining data, each is related differently to the evidence of social impact. In terms of the social impact coverage ratio, as shown in Table 13 , the most successful strategy is number 3 (searchable research results), as it has a relation of 17.86%, which is much higher than the ratios for the other 2 strategies. The second strategy (acronym search) is more effective than the first (profile accounts),with 1.77% for the former as opposed to 0.27% for the latter.

https://doi.org/10.1371/journal.pone.0203117.t013

Once tweets and Facebook posts providing linkages with information about social impact(ESISM)were identified, we classified them in terms of quantitative (QUANESISM) or qualitative evidence (QUALESISM)to determine which type of evidence was shared in social media. Table 14 indicates the amount of quantitative and qualitative evidence identified for each search strategy.

https://doi.org/10.1371/journal.pone.0203117.t014

First, the results obtained indicated that the SISM methodology aids in calculating the social impact coverage ratio of the research projects selected and evaluating whether the social impact of the corresponding research is shared by citizens in social media. The social impact coverage ratio applied to the sample selected is low, but when we analyse the SICOR of each project separately, we can observe that some projects have a higher social impact coverage ratio than others. Complementary to altmetrics measuring the extent to which research results reach out society, the SICOR considers the question whether this process includes evidence of potential or real social impact. In this sense, the overall methodology of SISM contributes to advancement in the evaluation of the social impact of research by providing a more precise approach to what we are evaluating.

This contribution complements current evaluation methodologies of social impact that consider which improvements are shared by citizens in social media. Exploring the results in more depth, it is relevant to highlight that of the ten projects selected, there is one research project with a social impact coverage ratio higher than those of the others, which include projects without any tweets or Facebook posts with evidence of social impact. This project has a higher ratio of evidence than the others because evidence of its social impact is shared more than is that of other projects. This also means that the researchers produced evidence of social impact and shared it during the project. Another relevant result is that the quantity of tweets and Fb/posts collected did not determine the number of tweets and Fb/posts found with evidence of social impact. Moreover, the analysis of the research projects selected showed that there are projects with less social media interaction but with more tweets and Fb/posts containing evidence of social media impact. Thus, the number of tweets and Fb/posts with evidence of social impact is not determined by the number of publication messages collected; it is determined by the type of messages published and shared, that is, whether they contain evidence of social impact or not.

The second main finding is related to the effectiveness of the search strategies defined. Related to the strategies carried out under this methodology, one of the results found is that the most effective search strategy is the searchable research results, which reveals a higher percentage of evidence of social impact than the own account and acronym search strategies. However, the use of these three search strategies is highly recommended because the combination of all of them makes it possible to identify more tweets and Facebook posts with evidence of social impact.

Another result is related to the type of evidence of social impact found. There is both quantitative and qualitative evidence. Both types are useful for understanding the type of social impact achieved by the corresponding research project. In this sense, quantitative evidence allows us to understand the improvements obtained by the implementation of the research results and capture their impact. In contrast, qualitative evidence allows us to deeply understand how the resultant improvements obtained from the implementation of the research results are evaluated by the end users by capturing their corresponding direct quotes. The social impact includes the identification of both real and potential social impact.

Conclusions

After discussing the main results obtained, we conclude with the following points. Our study indicates that there is incipient evidence of social impact, both potential and real, in social media. This demonstrates that researchers from different fields, in the present case involved in medical research, public health, animal welfare and genomics, are sharing the improvements generated by their research and opening up new venues for citizens to interact with their work. This would imply that scientists are promoting not only the dissemination of their research results but also the evidence on how their results may lead to the improvement of societies. Considering the increasing relevance and presence of the dissemination of research, the results indicate that scientists still need to include in their dissemination and communication strategies the aim of sharing the social impact of their results. This implies the publication of concrete qualitative or quantitative evidence of the social impact obtained. Because of the inclusion of this strategy, citizens will pay more attention to the content published in social media because they are interested in knowing how science can contribute to improving their living conditions and in accessing crucial information. Sharing social impact in social media facilitates access to citizens of different ages, genders, cultural backgrounds and education levels. However, what is most relevant for our argument here is how citizens should also be able to participate in the evaluation of the social impact of research, with social media a great source to reinforce this democratization process. This contributes not only to greatly improving the social impact assessment, as in addition to experts, policy makers and scientific publications, citizens through social media contribute to making this assessment much more accurate. Thus, citizens’ contribution to the dissemination of evidence of the social impact of research yields access to more diverse sectors of society and information that might be unknown by the research or political community. Two future steps are opened here. On the one hand, it is necessary to further examine the profiles of users who interact with this evidence of social impact considering the limitations of the privacy and availability of profile information. A second future task is to advance in the articulation of the role played by citizens’ participation in social impact assessment, as citizens can contribute to current worldwide efforts by shedding new light on this process of social impact assessment and contributing to making science more relevant and useful for the most urgent and poignant social needs.

Supporting information

S1 file. interrater reliability (kappa) result..

This file contains the SPSS file with the result of the calculation of Cohen’s Kappa regards the interrater reliability. The word document exported with the obtained result is also included.

https://doi.org/10.1371/journal.pone.0203117.s001

S2 File. Data collected and SICOR calculation.

This excel contains four sheets, the first one titled “data collected” contains the number of tweets and Facebook posts collected through the three defined search strategies; the second sheet titled “sample” contains the sample classified by project indicating the ID of the message or code assigned, the type of message (tweet or Facebook post) and the codification done by researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact); the third sheet titled “evidence found” contains the number of type of evidences of social impact founded by project (ESISM-QUANESIM or ESISM-QUALESIM), search strategy and type of message (tweet or Facebook posts); and the last sheet titled “SICOR” contains the Social Impact Coverage Ratio calculation by projects in one table and type of search strategy done in another one.

https://doi.org/10.1371/journal.pone.0203117.s002

Acknowledgments

The research leading to these results received funding from the 7 th Framework Programme of the European Commission under Grant Agreement n° 613202. The extraction of available data using the list of searchable keywords on Twitter and Facebook followed the ethical guidelines for social media research supported by the Economic and Social Research Council (UK) [ 36 ] and the University of Aberdeen [ 37 ]. Furthermore, the research results have already been published and made public, and hence, there are no ethical issues.

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Social Media as Research Instrument for Urban Planning and Design

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Analysing the Impact of Social Media on Students’ Academic Performance: A Comparative Study of Extraversion and Introversion Personality

Sourabh sharma.

International Management Institute (IMI), Bhubaneswar, India

Ramesh Behl

Associated data.

Complete data and material is available to support transparency.

The advent of technology in education has seen a revolutionary change in the teaching–learning process. Social media is one such invention which has a major impact on students’ academic performance. This research analyzed the impact of social media on the academic performance of extraversion and introversion personality students. Further, the comparative study between these two personalities will be analysed on education level (postgraduate and undergraduate) and gender (male and female). The research was initiated by identifying the factors of social media impacting students’ academic performance. Thereafter, the scale was developed, validated and tested for reliability in the Indian context. Data were collected from 408 students segregated into 202 males and 206 females. Two hundred and thirty-four students are enrolled in postgraduation courses, whereas 174 are registered in the undergraduate programme. One-way ANOVA has been employed to compare the extraversion and introversion students of different education levels and gender. A significant difference is identified between extraversion and introversion students for the impact of social media on their academic performance.

Introduction

Social Networking Sites (SNS) gained instant popularity just after the invention and expansion of the Internet. Today, these sites are used the most to communicate and spread the message. The population on these social networking sites (SNS) has increased exponentially. Social networking sites (SNS) in general are called social media (Boyd & Ellison, 2008 ). Social media (SM) is used extensively to share content, initiate discussion, promote businesses and gain advantages over traditional media. Technology plays a vital role to make SM more robust by reducing security threats and increasing reliability (Stergiou et al., 2018 ).

As of January 2022, more than 4.95 billion people are using the Internet worldwide, and around 4.62 billion are active SM users (Johnson, 2022 ). In India, the number of Internet users was 680 million by January 2022, and there were 487 million active social media users (Basuray, 2022 ). According to Statista Research Department ( 2022 ), in India, SM is dominated by two social media sites, i.e. YouTube and Facebook. YouTube has 467 million users followed by Facebook with 329 million users.

Although almost all age groups are using SM platforms to interact and communicate with their known community (Whiting & Williams, 2013 ), it has been found that social media sites are more popular among youngsters and specifically among students. They use SM for personal as well as academic activities extensively (Laura et al., 2017 ). Other than SM, from the last two years, several online platforms such as Microsoft Teams, Zoom and Google Meet are preferred to organize any kind of virtual meetings, webinars and online classes. These platforms were used worldwide to share and disseminate knowledge across the defined user community during the pandemic. Social media sites such as Facebook, YouTube, Instagram, WhatsApp and blogs are comparatively more open and used to communicate with public and/or private groups. Earlier these social media platforms were used only to connect with friends and family, but gradually these platforms became one of the essential learning tools for students (Park et al., 2009 ). To enhance the teaching–learning process, these social media sites are explored by all types of learning communities (Dzogbenuku et al., 2019 ). SM when used in academics has both advantages and disadvantages. Social media helps to improve academic performance, but it may also distract the students from studies and indulge them in other non-academic activities (Alshuaibi et al., 2018 ).

Here, it is important to understand that the personality traits of students, their education level and gender are critical constructs to determine academic performance. There are different personality traits of an individual such as openness, conscientiousness, extraversion and introversion, agreeableness and neuroticism (McCrae & Costa, 1987 ). This cross-functional research is an attempt to study the impact of social media on the academic performance of students while using extraversion and introversion personality traits, education levels and gender as moderating variables.

Literature Review

There has been a drastic change in the internet world due to the invention of social media sites in the last ten years. People of all age groups now share their stories, feelings, videos, pictures and all kinds of public stuff on social media platforms exponentially (Asur & Huberman, 2010 ). Youth, particularly from the age group of 16–24, embraced social media sites to connect with their friends and family, exchange information and showcase their social status (Boyd & Ellison, 2008 ). Social media sites have many advantages when used in academics. The fun element of social media sites always helps students to be connected with peers and teachers to gain knowledge (Amin et al., 2016 ). Social media also enhances the communication between teachers and students as this are no ambiguity and miscommunication from social media which eventually improves the academic performance of the students (Oueder & Abousaber, 2018 ).

When social media is used for educational purposes, it may improve academic performance, but some associated challenges also come along with it (Rithika & Selvaraj, 2013 ). If social media is incorporated into academics, students try to also use it for non-academic discussions (Arnold & Paulus, 2010 ). The primary reason for such distraction is its design as it is designed to be a social networking tool (Qiu et al., 2013 ). According to Englander et al. ( 2010 ), the usage of social media in academics has more disadvantages than advantages. Social media severely impacts the academic performance of a student. The addiction to social media is found more among the students of higher studies which ruins the academic excellence of an individual (Nalwa & Anand, 2003 ). Among the social media users, Facebook users’ academic performance was worse than the nonusers or users of any other social media network. Facebook was found to be the major distraction among students (Kirschner & Karpinski, 2010 ). However, other studies report contrary findings and argued that students benefited from chatting (Jain et al., 2012 ), as it improves their vocabulary and writing skills (Yunus & Salehi, 2012 ). Social media can be used either to excel in academics or to devastate academics. It all depends on the way it is used by the students. The good or bad use of social media in academics is the users’ decision because both the options are open to the students (Landry, 2014 ).

Kaplan and Haenlein ( 2010 ) defined social media as user-generated content shared on web 2.0. They have also classified social media into six categories:

• Social Networking Sites: Facebook, Twitter, LinkedIn and Instagram are the social networking sites where a user may create their profile and invite their friends to join. Users may communicate with each other by sharing common content.
• Blogging Sites: Blogging sites are individual web pages where users may communicate and share their knowledge with the audience.
• Content Communities and Groups: YouTube and Slideshare are examples of content communities where people may share media files such as pictures, audio and video and PPT presentations.
• Gaming Sites: Users may virtually participate and enjoy the virtual games.
• Virtual Worlds: During COVID-19, this type of social media was used the most. In the virtual world, users meet with each other at some decided virtual place and can do the pre-decided things together. For example, the teacher may decide on a virtual place of meeting, and students may connect there and continue their learning.
• Collaborative Content Sites: Wikipedia is an example of a collaborative content site. It permits many users to work on the same project. Users have all rights to edit and add the new content to the published project.

Massive open online courses (MOOCs) are in trend since 2020 due to the COVID-19 pandemic (Raja & Kallarakal, 2020 ). MOOCs courses are generally free, and anyone may enrol for them online. Many renowned institutions have their online courses on MOOCs platform which provides a flexible learning opportunity to the students. Students find them useful to enhance their knowledge base and also in career development. Many standalone universities have collaborated with the MOOCs platform and included these courses in their curriculum (Chen, 2013 ).

Security and privacy are the two major concerns associated with social media. Teachers are quite apprehensive in using social media for knowledge sharing due to the same concerns (Fedock et al., 2019 ). It was found that around 72% teachers were reluctant to use social media platforms due to integrity issues and around 63% teachers confirmed that security needs to be tightened before using social media in the classroom (Surface et al., 2014 ). Proper training on security and privacy, to use social media platforms in academics, is needed for  students and teachers (Bhatnagar & Pry, 2020 ).

The personality traits of a student also play a significant role in deciding the impact of social media on students’ academic performance. Personality is a dynamic organization which simplifies the way a person behaves in a situation (Phares, 1991 ). Human behaviour has further been described by many renowned researchers. According to Lubinski ( 2000 ), human behaviour may be divided into five factors, i.e. cognitive abilities, personality, social attitudes, psychological interests and psychopathology. These personality traits are very important characteristics of a human being and play a substantial role in work commitment (Macey & Schneider, 2008 ). Goldberg ( 1993 ) elaborated on five dimensions of personality which are commonly known as the Big Five personality traits. The traits are “openness vs. cautious”; “extraversion vs. introversion”; “agreeableness vs. rational”; “conscientiousness vs. careless”; and “neuroticism vs. resilient”.

It has been found that among all personality traits, the “extraversion vs. introversion” personality trait has a greater impact on students’ academic performance (Costa & McCrae, 1999 ). Extrovert students are outgoing, talkative and assertive (Chamorro et al., 2003 ). They are positive thinkers and comfortable working in a crowd. Introvert students are reserved and quiet. They prefer to be isolated and work in silos (Bidjerano & Dai, 2007 ). So, in the present study, we have considered only the “extraversion vs. introversion” personality trait. This study is going to analyse the impact of social media platforms on students’ academic performance by taking the personality trait of extraversion and introversion as moderating variables along with their education level and gender.

Research Gap

Past research by Choney ( 2010 ), Karpinski and Duberstein ( 2009 ), Khan ( 2009 ) and Kubey et al. ( 2001 ) was done mostly in developed countries to analyse the impact of social media on the students’ academic performance, effect of social media on adolescence, and addictiveness of social media in students. There are no published research studies where the impact of social media was studied on students’ academic performance by taking their personality traits, education level and gender all three together into consideration. So, in the present study, the impact of social media will be evaluated on students’ academic performance by taking their personality traits (extraversion and introversion), education level (undergraduate and postgraduate) and gender (male and female) as moderating variables.

Objectives of the Study

Based on the literature review and research gap, the following research objectives have been defined:

• To identify the elements of social media impacting student's academic performance and to develop a suitable scale
• To test the  validity and reliability of the scale
• To analyse the impact of social media on students’ academic performance using extraversion and introversion personality trait, education level and gender as moderating variables

Research Methodology

Sampling technique.

Convenience sampling was used for data collection. An online google form was floated to collect the responses from 408 male and female university students of undergraduation and postgraduation streams.

Objective 1 To identify the elements of social media impacting student's academic performance and to develop a suitable scale.

A structured questionnaire was employed to collect the responses from 408 students of undergraduate and postgraduate streams. The questionnaire was segregated into three sections. In section one, demographic details such as gender, age and education stream were defined. Section two contained the author’s self-developed 16-item scale related to the impact of social media on the academic performance of students. The third section had a standardized scale developed by John and Srivastava ( 1999 ) of the Big Five personality model.

Demographics

There were 408 respondents (students) of different education levels consisting of 202 males (49.5%) and 206 females (50.5%). Most of the respondents (87%) were from the age group of 17–25 years. 234 respondents (57.4) were enrolled on postgraduation courses, whereas 174 respondents (42.6) were registered in the undergraduate programme. The result further elaborates that WhatsApp with 88.6% and YouTube with 82.9% are the top two commonly used platforms followed by Instagram with 76.7% and Facebook with 62.3% of students. 65% of students stated that Google doc is a quite useful and important application in academics for document creation and information dissemination.

Validity and Reliability of Scale

Objective 2 Scale validity and reliability.

Exploratory factor analysis (EFA) and Cronbach’s alpha test were used to investigate construct validity and reliability, respectively.

The author’s self-designed scale of ‘social media impacting students’ academic performance’ consisting of 16 items was validated using exploratory factor analysis. The principle component method with varimax rotation was applied to decrease the multicollinearity within the items. The initial eigenvalue was set to be greater than 1.0 (Field, 2005 ). Kaiser–Meyer–Olkin (KMO) with 0.795 and Bartlett’s test of sphericity having significant values of 0.000 demonstrated the appropriateness of using exploratory factor analysis.

The result of exploratory factor analysis and Cronbach’s alpha is shown in Table ​ Table1. 1 . According to Sharma and Behl ( 2020 ), “High loading on the same factor and no substantial cross-loading confirms convergent and discriminant validity respectively”.

Exploratory factor analysis and Cronbach’s alpha for the self-developed scale of “Social media impact on academic performance”

The self-developed scale was segregated into four factors, namely “Accelerating Impact”, “Deteriorating Impact”, “Social Media Prospects” and “Social Media Challenges”.

The first factor, i.e. “Accelerating Impact”, contains items related to positive impact of social media on students’ academic performance. Items in this construct determine the social media contribution in the grade improvement, communication and knowledge sharing. The second factor “Deteriorating Impact” describes the items which have a negative influence of social media on students’ academic performance. Items such as addiction to social media and distraction from studies are an integral part of this factor. “Social Media Prospects” talk about the opportunities created by social media for students’ communities. The last factor “Social Media Challenges” deals with security and privacy issues created by social media sites and the threat of cyberbullying which is rampant in academics.

The personality trait of an individual always influences the social media usage pattern. Therefore, the impact of social media on the academic performance of students may also change with their personality traits. To measure the personality traits, the Big Five personality model was used. This model consists of five personality traits, i.e. “openness vs. cautious”; “extraversion vs. introversion”; “agreeableness vs. rational”; “conscientiousness vs. careless”; and “neuroticism vs. resilient”. To remain focussed on the scope of the study, only a single personality trait, i.e. “extraversion vs. introversion” with 6 items was considered for analysis. A reliability test of this existing scale using Cronbach’s alpha was conducted. Prior to the reliability test, reverse scoring applicable to the associated items was also calculated. Table ​ Table2 2 shows the reliability score, i.e. 0.829.

Cronbach’s alpha test for the scale of extraversion vs. introversion personality traits

Objective 3 To analyse the impact of social media on students’ academic performance using extraversion and introversion personality traits, education level and gender as moderating variables.

The research model shown in Fig.  1 helps in addressing the above objective.

Social media factors impacting academic performances of extraversion and introversion personality traits of students at different education levels and gender

As mentioned in Fig.  1 , four dependent factors (Accelerating Impact, Deteriorating Impact, Social Media Prospects and Social Media Challenges) were derived from EFA and used for analysing the impact of social media on the academic performance of students having extraversion and introversion personality traits at different education levels and gender.

Students having a greater average score (more than three on a scale of five) for all personality items mentioned in Table ​ Table2 2 are considered to be having extraversion personality or else introversion personality. From the valid dataset of 408 students, 226 students (55.4%) had extraversion personality trait and 182 (44.6%) had introversion personality trait. The one-way ANOVA analysis was employed to determine the impact of social media on academic performance for all three moderators, i.e. personality traits (Extraversion vs. Introversion), education levels (Undergraduate and Postgraduate) and gender (Male and Female). If the sig. value for the result is >  = 0.05, we may accept the null hypothesis, i.e. there is no significant difference between extraversion and introversion personality students for the moderators; otherwise, null hypothesis is rejected which means there is a significant difference for the moderators.

Table ​ Table3 3 shows the comparison of the accelerating impact of social media on the academic performance of all students having extraversion and introversion personality traits. It also shows a comparative analysis on education level and gender for these two personality traits of students. In the first comparison of extraversion and introversion students, the sig. value is 0.001, which indicates that there is a significant difference among extraversion and introversion students for the “Accelerating Impact” of social media on academic performance. Here, 3.781 is the mean value for introversion students which is higher than the mean value 3.495 of extraversion students. It clearly specifies that the accelerating impact of social media is more prominent in the students having introversion personality traits. Introversion students experienced social media as the best tool to express thoughts and improve academic grades. The result is also consistent with the previous studies where introvert students are perceived to use social media to improve their academic performance (Amichai-Hamburger et al., 2002 ; Voorn & Kommers, 2013 ). Further at the education level, there was a significant difference in postgraduate as well as undergraduate students for the accelerating impact of social media on the academic performance among students with extraversion and introversion, and introverts seem to get better use of social media. The gender-wise significant difference was also analysed between extraversion and introversion personalities. Female introversion students were found to gain more of an accelerating impact of social media on their academic performance.

One-way ANOVA: determining “Accelerating Impact” among extraversion and introversion personality traits students at different education levels and genders

Significant at the 0.05 level

Like Table ​ Table3, 3 , the first section of Table ​ Table4 4 compares the deteriorating impact of social media on the academic performance of all students having extraversion and introversion personality traits. Here, the sig. value 0.383 indicates no significant difference among extraversion and introversion students for the “Deteriorating Impact” of social media on academic performance. The mean values show the moderating deteriorating impact of social media on the academic performance of extraversion and introversion personality students. Unlimited use of social media due to the addiction is causing a distraction in academic performance, but the overall impact is not on the higher side. Further, at the education level, the sig. values 0.423 and 0.682 of postgraduate and undergraduate students, respectively, show no significant difference between extraversion and introversion students with respect to “Deteriorating Impact of Social Media Sites”. The mean values again represent the moderate impact. Gender-wise, male students have no difference between the two personality traits, but at the same time, female students have a significant difference in the deteriorating impact, and it is more on extroverted female students.

One-way ANOVA: Examining “Deteriorating Impact” among extraversion and introversion personality traits students at different education levels and genders

The significant value, i.e. 0.82, in Table ​ Table5 5 represents no significant difference between extraversion and introversion personality students for the social media prospects. The higher mean value of both personality students indicates that they are utilizing the opportunities of social media in the most appropriate manner. It seems that all the students are using social media for possible employment prospects, gaining knowledge by attending MOOCs courses and transferring knowledge among other classmates. At the education level, postgraduation students have no significant difference between extraversion and introversion for the social media prospects, but at the undergraduate level, there is a significant difference among both the personalities, and by looking at mean values, extroverted students gain more from the social media prospects. Gender-wise comparison of extraversion and introversion personality students found no significant difference in the social media prospects for male as well as female students.

One-way ANOVA: Examining “Social Media Prospects” among extraversion and introversion personality traits students at different education levels and genders

Table ​ Table6 6 shows the comparison of the social media challenges of all students having extraversion and introversion personality traits. It is also doing a comparative analysis on education level and gender for these two personality traits of students. All sig. values in Table ​ Table6 6 represent no significant difference between extraversion and introversion personality students for social media challenges. Even at the education level and gender-wise comparison of the two personalities, no significant difference is derived. The higher mean values indicate that the threat of cyberbullying, security and privacy is the main concern areas for extraversion and introversion personality students. Cyberbullying is seen to be more particularly among female students (Snell & Englander, 2010 ).

One-way ANOVA: Examining “Social Media Challenges” among extraversion and introversion personality traits students at different education levels and genders

The use of social media sites in academics is becoming popular among students and teachers. The improvement or deterioration in academic performance is influenced by the personality traits of an individual. This study has tried to analyse the impact of social media on the academic performance of extraversion and introversion personality students. This study has identified four factors of social media which have an impact on academic performance. These factors are: accelerating impact of social media; deteriorating impact of social media; social media prospects; and social media challenges.

Each of these factors has been used for comparative analysis of students having extraversion and introversion personality traits. Their education level and gender have also been used to understand the detailed impact between these two personality types. In the overall comparison, it has been discovered that both personalities (extraversion and introversion) have a significant difference for only one factor, i.e. “Accelerating Impact of Social Media Sites” where students with introversion benefited the most. At the education level, i.e. postgraduate and undergraduate, there was a significant difference between extraversion and introversion personalities for the first factor which is the accelerating impact of social media. Here, the introversion students were found to benefit in postgraduate as well as undergraduate courses. For the factors of deteriorating impact and social media challenges, there was no significant difference between extraversion and introversion personality type at the different education levels.

Surprisingly, for the first factor, i.e. the accelerating impact of social media, in gender-wise comparison, no significant difference was found between extraversion and introversion male students. Whereas a significant difference was found in female students. The same was the result for the second factor, i.e. deteriorating impact of social media of male and female students. For social media prospects and social media challenges, no significant difference was identified between extraversion and introversion students of any gender.

Findings and Implications

The personality trait of a student plays a vital role in analysing the impact of social media on their academic performance. The present study was designed to find the difference between extraversion and introversion personality types in students for four identified factors of social media and their impact on students’ academic performance. The education level and gender were also added to make it more comprehensive. The implications of this study are useful for institutions, students, teachers and policymakers.

This study will help the institutions to identify the right mix of social media based on the personality, education level and gender of the students. For example, technological challenges are faced by all students. It is important for the institutions to identify the challenges such as cyberbullying, security and privacy issues and accordingly frame the training sessions for all undergraduate and postgraduate students. These training sessions will help students with extraversion and introversion to come out from possible technological hassles and will create a healthy ecosystem (Okereke & Oghenetega, 2014 ).

Students will also benefit from this study as they will be conscious of the possible pros and cons that exist because of social media usage and its association with students’ academic performance. This learning may help students to enhance their academic performance with the right use of social media sites. The in-depth knowledge of all social media platforms and their association with academics should be elucidated to the students so that they may explore the social media opportunities in an optimum manner. Social media challenges also need to be made known to the students to improve upon and overcome with time (Boateng & Amankwaa, 2016 ).

Teachers are required to design the curriculum by understanding the learning style of students with extraversion and introversion personality type. Innovation and customization in teaching style are important for the holistic development of students and to satisfy the urge for academic requirements. Teachers should also guide the students about the adverse impacts of each social media platform, so that these can be minimized. Students should also be guided to reduce the time limit of using social media (Owusu-Acheaw & Larson, 2015 ).

Policymakers are also required to understand the challenges faced by the students while using social media in academics. All possible threats can be managed by defining and implementing transparent and proactive policies. As social media sites are open in nature, security and privacy are the two major concerns. The Government of India should take a strong stand to control all big social media companies so that they may fulfil the necessary compliances related to students’ security and privacy (Kumar & Pradhan, 2018 ).

The overall result of these comparisons gives a better insight and deep understanding of the significant differences between students with extraversion and introversion personality type towards different social media factors and their impact on students’ academic performance. Students’ behaviour according to their education level and gender for extraversion and introversion personalities has also been explored.

Limitation and Future Scope of Research

Due to COVID restrictions, a convenient sampling technique was used for data collection which may create some response biases where the students of introversion personality traits may have intentionally described themselves as extroversion personalities and vice versa. This study also creates scope for future research. In the Big Five personality model, there are four other personality traits which are not considered in the present study. There is an opportunity to also use cross-personality comparisons for the different social media parameters. The other demographic variables such as age and place may also be explored in future research.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. SS and Prof. RB. The first draft of the manuscript was written by Dr. SS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

No funds, grants, or other support was received.

Availability of data and material

Declarations.

The authors declare that they have no conflict of interest.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Verbal informed consent was obtained from the participants.

Verbal consent is obtained for publication

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Sourabh Sharma, Email: ni.ude.hbimi@hbaruos .

Ramesh Behl, Email: ude.imi@lhebr .

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• Social Media Use in 2021

A majority of Americans say they use YouTube and Facebook, while use of Instagram, Snapchat and TikTok is especially common among adults under 30.

Table of contents.

• Acknowledgments
• Methodology

To better understand Americans’ use of social media, online platforms and messaging apps, Pew Research Center surveyed 1,502 U.S. adults from Jan. 25 to Feb. 8, 2021, by cellphone and landline phone. The survey was conducted by interviewers under the direction of Abt Associates and is weighted to be representative of the U.S. adult population by gender, race, ethnicity, education and other categories. Here are the  questions used for this report , along with responses, and  its methodology .

Despite a string of controversies and the public’s relatively negative sentiments about aspects of social media, roughly seven-in-ten Americans say they ever use any kind of social media site – a share that has remained relatively stable over the past five years, according to a new Pew Research Center survey of U.S. adults.

Beyond the general question of overall social media use, the survey also covers use of individual sites and apps. YouTube and Facebook continue to dominate the online landscape, with 81% and 69%, respectively, reporting ever using these sites. And YouTube and Reddit were the only two platforms measured that saw statistically significant growth since 2019 , when the Center last polled on this topic via a phone survey.

When it comes to the other platforms in the survey, 40% of adults say they ever use Instagram and about three-in-ten report using Pinterest or LinkedIn. One-quarter say they use Snapchat, and similar shares report being users of Twitter or WhatsApp. TikTok – an app for sharing short videos – is used by 21% of Americans, while 13% say they use the neighborhood-focused platform Nextdoor.

Even as other platforms do not nearly match the overall reach of YouTube or Facebook, there are certain sites or apps, most notably Instagram, Snapchat and TikTok, that have an especially strong following among young adults. In fact, a majority of 18- to 29-year-olds say they use Instagram (71%) or Snapchat (65%), while roughly half say the same for TikTok.

These findings come from a nationally representative survey of 1,502 U.S. adults conducted via telephone Jan. 25-Feb.8, 2021.

With the exception of YouTube and Reddit, most platforms show little growth since 2019

YouTube is the most commonly used online platform asked about in this survey, and there’s evidence that its reach is growing. Fully 81% of Americans say they ever use the video-sharing site, up from 73% in 2019. Reddit was the only other platform polled about that experienced statistically significant growth during this time period – increasing from 11% in 2019 to 18% today. 

Facebook’s growth has leveled off over the last five years, but it remains one of the most widely used social media sites among adults in the United States: 69% of adults today say they ever use the site, equaling the share who said this two years prior.  

Similarly, the respective shares of Americans who report using Instagram, Pinterest, LinkedIn, Snapchat, Twitter and WhatsApp are statistically unchanged since 2019 . This represents a broader trend that extends beyond the past two years in which the rapid adoption of most of these sites and apps seen in the last decade has slowed. (This was the first year the Center asked about TikTok via a phone poll and the first time it has surveyed about Nextdoor.)

Adults under 30 stand out for their use of Instagram, Snapchat and TikTok

When asked about their social media use more broadly – rather than their use of specific platforms – 72% of Americans say they ever use social media sites.

In a pattern consistent with past Center studies on social media use, there are some stark age differences. Some 84% of adults ages 18 to 29 say they ever use any social media sites, which is similar to the share of those ages 30 to 49 who say this (81%). By comparison, a somewhat smaller share of those ages 50 to 64 (73%) say they use social media sites, while fewer than half of those 65 and older (45%) report doing this.

These age differences generally extend to use of specific platforms, with younger Americans being more likely than their older counterparts to use these sites – though the gaps between younger and older Americans vary across platforms.

Majorities of 18- to 29-year-olds say they use Instagram or Snapchat and about half say they use TikTok, with those on the younger end of this cohort – ages 18 to 24 – being especially likely to report using Instagram (76%), Snapchat (75%) or TikTok (55%). 1 These shares stand in stark contrast to those in older age groups. For instance, while 65% of adults ages 18 to 29 say they use Snapchat, just 2% of those 65 and older report using the app – a difference of 63 percentage points.

Additionally, a vast majority of adults under the age of 65 say they use YouTube. Fully 95% of those 18 to 29 say they use the platform, along with 91% of those 30 to 49 and 83% of adults 50 to 64. However, this share drops substantially – to 49% – among those 65 and older.

By comparison, age gaps between the youngest and oldest Americans are narrower for Facebook. Fully 70% of those ages 18 to 29 say they use the platform, and those shares are statistically the same for those ages 30 to 49 (77%) or ages 50 to 64 (73%). Half of those 65 and older say they use the site – making Facebook and YouTube the two most used platforms among this older population.

Other sites and apps stand out for their demographic differences:

• Instagram: About half of Hispanic (52%) and Black Americans (49%) say they use the platform, compared with smaller shares of White Americans (35%) who say the same. 2
• WhatsApp: Hispanic Americans (46%) are far more likely to say they use WhatsApp than Black (23%) or White Americans (16%). Hispanics also stood out for their WhatsApp use in the Center’s previous surveys on this topic.
• LinkedIn: Those with higher levels of education are again more likely than those with lower levels of educational attainment to report being LinkedIn users. Roughly half of adults who have a bachelor’s or advanced degree (51%) say they use LinkedIn, compared with smaller shares of those with some college experience (28%) and those with a high school diploma or less (10%).
• Pinterest: Women continue to be far more likely than men to say they use Pinterest when compared with male counterparts, by a difference of 30 points (46% vs. 16%).
• Nextdoor: There are large differences in use of this platform by community type. Adults living in urban (17%) or suburban (14%) areas are more likely to say they use Nextdoor. Just 2% of rural Americans report using the site.

A majority of Facebook, Snapchat and Instagram users say they visit these platforms on a daily basis

While there has been much written about Americans’ changing relationship with Facebook , its users remain quite active on the platform. Seven-in-ten Facebook users say they use the site daily, including 49% who say they use the site several times a day. (These figures are statistically unchanged from those reported in the Center’s 2019 survey about social media use.)  

Smaller shares – though still a majority – of Snapchat or Instagram users report visiting these respective platforms daily (59% for both). And being active on these sites is especially common for younger users. For instance, 71% of Snapchat users ages 18 to 29 say they use the app daily, including six-in-ten who say they do this multiple times a day. The pattern is similar for Instagram: 73% of 18- to 29-year-old Instagram users say they visit the site every day, with roughly half (53%) reporting they do so several times per day.

YouTube is used daily by 54% if its users, with 36% saying they visit the site several times a day. By comparison, Twitter is used less frequently, with fewer than half of its users (46%) saying they visit the site daily.

• Due to a limited sample size, figures for those ages 25 to 29 cannot be reported on separately. ↩
• There were not enough Asian American respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout this report. ↩

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• Business and management
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In a globalised society, characterised by increasingly demanding markets and the accelerated growth of the digital approach, sports organisations face the challenge of connecting with fans, generating and maintaining audiences and communicating with stakeholders creatively and efficiently. Social media has become a fundamental tool, with engagement as a critical measurement element. However, despite its popularity and use, many questions about its application, measurement and real potential in the sports sector still need to be answered. Therefore, the main objective of this study is to carry out a descriptive and comparative analysis of the engagement generated through social media posts by elite football clubs in Europe, South America and North America. To this purpose, 19,745 Facebook, Twitter and Instagram posts were analysed, through the design, validation and application of an observation instrument, using content analysis techniques. The findings show evidence of a priority focus on “Marketing” and “Sports” type messages in terms of frequency, with high engagement rates. They were also showing a growing stream of “ESG” type messages, with a low posting frequency but engagement rates similar to “Marketing” and “Sport”. “Institutional” messages remain constant in all football clubs. “Commercial” messages still have growth potential in both regards, frequency and engaging fans, representing an opportunity for digital assets. Also, specific format combinations that generate greater engagement were identified: “text/image” and “text/videos” are the format combinations more used by football clubs on Facebook, Twitter and Instagram; however, resulting in different engagement rates. This study showed evidence of different social media management strategies adopted according to region, obtaining similar engagement rates. This research concludes with theoretical and practical applications that will be of interest to both academics and practitioners to maximise the potential of social media for fan engagement, social initiatives and as a marketing tool.

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Introduction.

In a context of booming technology and high organisational competitiveness (Ratten, 2020 ), digital tools have evolved from an essential add-on to crucial strategic and operational elements in sports organisations (Stegmann et al., 2021 ). Fans increasingly demand a connection with their favourite athletes and teams (Su et al., 2020 ) through digital channels such as social media, podcasts (Rohden et al., 2023 ), Esports (Cuesta-Valiño et al., 2022 ), among others. Today’s digitised world presents therefore, an opportunity for brands, sponsors, sports properties, and other stakeholders to interact in a complex and emotionally charged sector (Su et al., 2022 ) for fans from different age generations (Sheldon et al., 2021 ). Understanding and getting to know fans are at the forefront of every sports organisation’s objective.

Social media plays a fundamental role due to their ability to reach multiple audiences faster and generate a sense of connection with fans through a key measurement element: engagement (Doyle et al., 2022 ). Sports organisations, specifically football clubs, invest time, people and resources in managing social media to achieve their brand positioning and commercial and communication objectives (Anagnostopoulos et al., 2018 ; Maderer et al., 2018 ), with Facebook, Twitter and more recently, Instagram, being the most widely used (Abeza et al., 2019 ; Machado et al., 2020 ). However, the real potential of social media and its optimal use still poses many questions to be answered.

Although there are previous studies that have explored some aspects of social media in a sports context (e.g., Anagnostopoulos et al., 2018 ; Mastromartino and Naraine, 2022 ; Su et al., 2020 ), the potential impact and efficiency of content posted by football clubs on their social media channels remains unclear. For example, several studies point to various factors that contribute to fan engagement on social media depending on elements such as the type of content, the format used (e.g. photo, text or a combination of both) or the social media platform (see Einsle et al., 2023 ; Maderer et al., 2018 ; Su et al., 2020 ). This gap in the literature prompts a call to action from across the domains of sports marketing and sports management. Identifying the elements generated by football clubs on their official social media profiles can help them improve their marketing strategies and better support their fans. Based on this need and opportunity for management improvement, this study addresses the following research question:

RQ . What are the main characteristics of Facebook, Twitter, and Instagram posts from elite football clubs to understand the content type, format and social media platform that generate the highest engagement among social media consumers?

Grounded on the theoretical framework of relationship marketing, the main objective of this study is to carry out a descriptive and comparative analysis of the engagement generated through social media posts on Facebook, Twitter and Instagram by elite football clubs in Europe, South America and North America, using a categorisation approach developed from an existing model in the literature (see Solanellas et al., 2022 ), as well as the identification of key elements of high-impact social media posts. For this purpose, a new instrument was designed, validated and applied to analyse the use of social media as a marketing tool in sports management. By conducting this exploration, this paper contributes to the literature on sports marketing by identifying which social media and which types of content provoke the most interaction among fans. As a result, football team managers can gain a better understanding of how to target and personalise potential commercial and branding actions, thereby reinforcing the loyalty and commitment of fans to football clubs, and opening or consolidating new lines of action aligned with the strategic objectives of sport entities. Furthermore, the findings and conclusions presented in this study can assist sports managers in the decision-making process, as well as in planning, organising, directing, and effectively controlling social media platforms, thus enhancing engagement with fans in a digital environment.

The article is structured as follows. Firstly, the literature review presents the main theoretical and conceptual elements, focusing on social media and their relationship with marketing theory in sports and football. Secondly, the methodological aspects guiding the study’s process are detailed, including sample, instrument, research procedure, and data analysis. Thirdly, the study’s main results are presented. Fourth, the discussion section critically examines the findings in the context of existing literature, offering practical and theoretical implications for both academics and practitioners. Finally, the study concludes with the main conclusions and limitations.

Literature review

Social media and sports, a combination of great potential.

Social media is a collective term for media tools, platforms, and applications allowing consumers to connect, communicate, and collaborate (Williams and Chinn, 2010 ). They encourage interaction between users and the organisation and provide information from customers and the organisation faster than through conventional media (Kümpel et al., 2015 ; Shilbury et al., 2014 ). Furthermore, social media is considered a mass phenomenon due to its ability to transmit information in an agile and interactive way (Vivar, 2009 ), as well as a unique form of communication that transcends geographical and social boundaries through the instantaneous communication of information (Filo et al., 2015 ). Social Media is used in different sectors for marketing activities (Chen, 2023 ), brand equity and loyalty (Malarvizhi et al., 2022 ) to understand consumer´s behaviour, brand positioning, business revenue opportunities and social communication (Ramos et al., 2019 ). However, although the first studies about this phenomenon have been explored in the sports industry field, there is still a need for more evidence about its real potential, essential elements, and efficiency measurement in the sector.

Due to the high graphic, interactive and visual content of social media, their use in the sports industry, a sector of strong emotional influence, has become more relevant and pervasive in the last decade (Hull and Abeza, 2021 ), where the interest of the viewer has become crucial and increasingly demanding (Nisar et al. 2018 ). The differences that make the sports industry unique and particular are, among others: immediate results and changes (Davis and Hilbert, 2013 ) in addition to the fact that every decision is “in the spotlight” of the public (alluding to the complexity of fans, athletes, coaches, media and other stakeholders). Thus, athletes, teams and sports organisations have been using social media as part of their public relations and communication efforts (Filo et al., 2015 ; Pegoraro, 2010 ; Yan et al., 2019 ) to engage with their partners and fans (Zakerian et al., 2022 ), promoting interactions and increasing engagement with the sport product, as well as with the team in general (Abeza et al., 2019 ; Parganas and Anagnostopoulos, 2015 ).

The linking of social media within the integrated marketing communication process has changed communication strategies and consumer outreach, where marketing managers must include these tools when developing and executing their customer-focused promotional strategies (Lee and Kahle, 2016 ; Rehman et al., 2022 ). On the other hand, social media, directly and indirectly, impacts revenue generation and favours negotiation with sponsors due to their notoriety, visibility, and reach (Mastromartino and Naraine, 2022 ; Parganas and Anagnostopoulos, 2015 ). They are therefore considered a key tool for building and enhancing a brand’s reputation (Maderer et al., 2018 ) and an ideal platform to advertise and increase the visibility of a brand or company, as well as to interact with and analyse the actions of their fans and followers (Abeza et al., 2017 ; García-Fernández et al., 2015 ; Herrera-Torres et al., 2017 ).

Social media has also been used in sports education in recent years (Sanz-Labrador et al., 2021 ). Moreover, their application is increasingly common in construction and dissemination related to social responsibility (López-Carril and Anagnostopoulos, 2020 ; Sharpe et al., 2020 ). In this way, they have also become a key tool for interacting with fans, addressing a strengthened social approach, and gaining engagement from athletes, sponsors, and authorities (Einsle et al., 2023 ; Oviedo et al., 2014 ; Su et al., 2020 ). Beyond the digital environment, Cuesta-Valiño et al. ( 2021 ) pointed out the relevance of considering the emerging sustainable management approach to measure sports organisations’ goals. One of the most relevant challenges for this industry is to issue social media posts efficiently, using the proper formatting resources and at the right time, to generate the most significant possible impact and engagement.

Relationship marketing theory applied to social media in sports

The sports industry is a fast-growing and increasingly diverse market worldwide (Kim and Andrew, 2016 ). Football (soccer in North America) is one of the most popular sports worldwide as well as a cultural manifestation, characterised by its high emotional level and economic, political and social relevance (Bucher and Eckl, 2022 ; Petersen-Wagner and Ludvigsen, 2022 ). Only in Spain, the sports sector generates 3.3% of the Gross Domestic Product (GDP), of which 1.37% is produced through football (PWC, 2020 ).

Globalisation has demanded an adaptation at all levels due to the endless search for immediacy and access to information, where the business of sports is becoming more and more relationship-based and the importance of generating engagement (Einsle et al., 2023 ; Fried and Mumcu, 2017 ; García-Fernández et al., 2017 ) is one of the most relevant variables in generating loyalty in sports organisations (Loranca-Valle et al., 2021 ; Núñez-Barriopedro et al., 2021 ). Sports consumers are seen as “channels” through which sports products can be promoted (O’Shea and Alonso, 2011 ), and sports fans have become both the consumer and the advocates of the product. This is where relationship marketing theory helps us to better understand this phenomenon. As Abeza and Sanderson ( 2022 , p. 287) point out, relationship marketing theory “is based on the idea that a relationship between two parties creates additional value for those involved”. This theory is one of the most widely used to understand the phenomenon of social media in sports (Abeza and Sanderson, 2022 ) as highlighted by numerous authors who have used it in their studies (e.g., Abeza et al., 2017 , 2019 , 2020 ; Su et al., 2020 ; Williams and Chinn, 2010 ).

Merging the roots of relationship marketing theory (Möller and Halinen, 2000 ) and the particular characteristics of the sports sector, and taking into account the perspective of short-term transactions and immediate economic benefits (Abeza et al., 2017 ), social media represents opportunities for better knowledge about fans, more advanced consumer–organisation interaction, efficient fan engagement, efficient use of resources and agile evaluation of the relationship between fans and organisation (Abeza et al., 2019 , 2020 ). In view of this, and in line with Abeza and Sanderson ( 2022 ), social media thus becomes a channel through which to establish, maintain and cultivate long-term relationships beneficial to both parties (in our study, football clubs and fans).

Previous studies have addressed the use of specific social media in the context of sports, such as Facebook (Achen, 2019 ; Meng et al., 2015 ; Pegoraro et al., 2017 ; Waters et al., 2009 ), Twitter (Blaszka et al., 2012 ; Hambrick et al., 2010 ; Lovejoy and Saxton, 2012 ; Winand et al., 2019 ; Witkemper et al., 2012 ) and Instagram (Anagnostopoulos et al., 2018 ; Machado et al., 2020 ; Zakerian et al., 2022 ), because of the relevance in the use of these platforms in the sports sector. From another broader perspective, Solanellas et al. ( 2022 ) propose a practical analysis of multiple social media in sports organisations from a content categorisation point of view.

The results and contributions of the studies mentioned above, reveal the importance of further exploring the social media fan engagement phenomenon as a strategic perspective (Tafesse and Wien, 2018 ) and the added value that social media can generate in sports. In this sense, it is relevant for sports managers to know which techniques, methodologies and perspectives to use. Furthermore, as stated by Abeza and Sanderson ( 2022 ), it is necessary to go deeper into the theories behind its use. Taking these aspects into account, this work presents a new instrument of observation and measurement of social media posts by football organisations, as a basis for understanding and deepening the knowledge about the digital audience and its impact on the different objectives of the organisation. Thus, the study draws on relationship marketing theory to better understand how sports managers can make the most of the possibilities offered by social media to generate added value from the interaction between fans and football clubs. Particularly, the developed instrument focuses on the analysis of the type of content published by football clubs, categorising it into dimensions, as well as the engagement of the different publications according to the type of dimension to which they belong.

With a view to the implementation of the instrument, and to contribute to the literature related to the use of social media as a marketing tool in sports, this study analyses Facebook, Twitter and Instagram posts issued by elite football clubs from Europe, South America and North America, using a practical approach to content categorisation and taking the engagement factor as a key element for comparison.

Methodology

This study adopts an exploratory, descriptive, and comparative research design (Andrew et al., 2011 ) using the observational method and content analysis techniques. Content analysis involves the recounting and comparison of content, followed by the interpretation of the underlying context. It has been widely used in social media communication research, specifically in sports settings (e.g., Anagnostopoulos et al., 2018 ; Wang and Zhou, 2015 ; Winand et al., 2019 ), to interpret textual data through systematic classification, coding, and identifying themes or patterns (Hsieh and Shannon, 2005 ). First, exploratory studies are particularly useful when the phenomenon under investigation is in constant evolution (such as social media as a marketing tool), as well as when there are several factors and variables at play (Andrew et al., 2011 ). In this study, these are linked to the engagement that can be caused by the type of content or format used by elite football clubs on their social media accounts. Second, the descriptive aspect of the research design aims to describe and quantify the engagement levels in social media for the selected football clubs. By Collecting and analysing quantitative data on the interaction metrics, including likes, comments, shares, and follower counts, the study provided a comprehensive overview of the current state of engagement, and other variables, among the clubs, helping to build a foundation for further analysis and comparison. Lastly, the comparative aspect of the research design (Andrew et al., 2011 ) is valuable in this study because it enables a cross-regional analysis of three of the most traditional social media platforms. The study compared the engagement practices, elements, and strategies across three key regions of the football industry worldwide. Understanding potential differences can be useful for sports managers to design more optimised social media marketing strategies.

Considering the study design and observational method applied in this research (Anguera-Argilaga et al., 2011 ), a nonprobable sample design (see Battaglia, 2008 ) was established following several steps to make the following three decisions: (1) selection of football clubs, (2) social media platforms, and (3) period of time studied.

First, a geographical criterion was used to determine the origin of the football clubs under study. This criterion was based on a comprehensive and global perspective, considering factors such as historical significance, popularity, sporting achievements, and the modernisation of football worldwide. Based on these considerations, three regions were selected for analysis: Europe and South America, renowned for their broad global relevance and football tradition (e.g., the winning national teams of the 22 editions of the FIFA World Cup so far are from Europe and South America [Venkat, 2023 ]). Next, North America was chosen for its ascending market growth potential and global efforts to promote football. This is exemplified by upcoming milestones, such as the organisation of the FIFA World Cup 2026 in the United States, Mexico, and Canada, as well as the recent arrival of Lionel Messi into Major League Soccer (see Mizrahi, 2023 ). These three regions are governed by the three most influential regional football bodies of FIFA: Europe (UEFA), South America (CONMEBOL), and North America (CONCACAF). Second, to select the most relevant football clubs in these three regions, we followed some of the selection criteria set in similar studies (e.g., Anagnostopoulos et al., 2018 ; Maderer et al., 2018 ). Therefore, the rankings of four of the most influential football organisations or websites were considered: (1) the International Federation of Football History and Statistics (IFFHS) club ranking, (2) the Football World Rankings website, (3) the FIFA club and league ranking, and (4) the Transfermarkt player ranking website (of great relevance in the player transfer market). As a result of this process, 24 teams were pre-selected (9 from Europe, 9 from South America and 6 from North America) according to the objectives and the study design and the author’s agreement (Andrew et al., 2011 ; Anguera-Argilaga et al., 2011 ; Battaglia, 2008 ; Hernández-Sampieri et al., 2014 ). Finally, a random draw was made resulting in a selection of six teams from Europe, six from South America and four from North America (with a limit of two teams per league). This process resulted in the 16 teams whose use of social media is analysed in this study (see Table 1 ).

Following, social media to be analysed in the study were selected. It was noted in the literature that Facebook had been one of the first social media to be used by football clubs and other sports organisations, either to connect with fans or purely for informational purposes (Achen, 2019 ; Waters et al., 2009 ). Twitter and Instagram are also platforms that have become relevant, not only for marketers in sports but also in other sectors (Anagnostopoulos et al., 2018 ; Wang and Zhou, 2015 ). Although the use of Facebook, Twitter and Instagram as marketing tools for football clubs has been studied (e.g., Machado et al. 2020 ; Maderer et al. 2018 ; Nisar et al., 2018 ), there is a lack of literature comparing their potential engagement across a sample of teams from different geographic regions. Thus, it was deemed appropriate to select these three social media sources for our study.

Finally, the periods over which the publications were to be extracted were determined. Among other authors, Ashley and Tuten ( 2015 ) point out that, in a social media environment, two to four weeks are sufficient for a wide variety of posts to be made in a regular and cyclical context, excluding exceptional milestones or events that could have an extraordinary impact on engagement and that could bias regular reading. Therefore, 45 days for each club and each social media is set as an appropriate observation period.

Once the sample selection criteria had been defined, the links of all publications from the clubs selected in the study on the three social media were extracted through the Fanpage Karma software that allows data to be collected and interpreted (Lozano-Blasco et al., 2021 ). After prior data analysis, the final sample consisted of 19,745 publications, a very similar figure to that used in other related studies (e.g., Maderer et al., 2018 ; Yan et al., 2019 ).

Instrument and research procedure

Based on the review of the techniques and methodologies used to analyse the use of social media as a marketing tool for football clubs in previous studies, we proceeded to design and develop an observation and data collection instrument in a Microsoft Excel Spreadsheet (.xlsx format), taking as a starting point the model of content analysis proposed by Solanellas et al. ( 2022 ). Due to the nature of the study, the .xlsx data collection format was chosen for its flexibility, allowing for manual data collection and the application of the categorisation tool post-by-post. This format has been successfully used as a data collection tool in previous social media content analysis studies in football (e.g., López-Carril and Anagnostopoulos, 2020 ).

To ensure its rigour, the codebook was subsequently submitted for review to nine field experts. The selection of these experts was undertaken via judgmental nonprobability sampling, a method commonly employed in the literature due to the specialised and ever-evolving nature of the subject (Andrew et al., 2011 ). These individuals were chosen based on specific criteria, encompassing their professional roles in specialised, coordinating, managerial, or directorial positions tied to the digital domain. Moreover, their academic background, particularly in marketing, methodology, or digital tools, was considered. To ensure an extensive grasp of the subject matter, the chosen experts were required to have a minimum of five years of experience in the area and to be actively participating in their respective roles. This approach aimed to incorporate diverse viewpoints, offering insights from a spectrum of angles relevant to this research. As a result, the panel of experts was comprised of the following professionals: the Head of Digital from a prominent European professional football league (1), a Marketing Manager and an International Communications Manager from leading professional football clubs (2), Directors of digital marketing and branding agencies (2), professors specialising in marketing and sports management at Spanish universities (2), and the Vice-President of Sales along with the Head of Digital from sports business intelligence consultancies (2).

Semi-structured interviews were undertaken with these chosen experts to delve into pertinent aspects linked to the study. An interview guide was developed, following the methodological aspects indicated in specialised works in this field (see Andrew et al., 2011 ; Anguera-Argilaga et al., 2011 ). Furthermore, the interview guide encompassed critical aspects of social media management and relevant facets of football club management (e.g., post formats, observation timeframes, platforms for capturing and analysing social media posts), drawing upon the elements and variables derived from studies conducted by Parganas and Anagnostopoulos ( 2015 ) as well as Solanellas et al. ( 2022 ). Additionally, these interviews comprised discussions about the conception and execution of the observation tool, which was employed as a supplementary instrument for data collection. Further variables relevant to the research objectives were explored within these interviews.

The qualitative insights garnered from the experts’ conclusive remarks offered valuable suggestions that contributed to refining the study’s development and enhancing the observation tool. This iterative approach ensured the harmonisation of the tool with the research objectives and its effective alignment with the study’s research questions. After incorporating the modifications suggested in the experts’ evaluations, the study’s codebook adhered to the variables and categories illustrated in Table 2 .

The .xlsx instrument sheet was then pilot-tested. Seventy-five publications (25 from Facebook, 25 from Instagram and 25 from Twitter) from three different football clubs were randomly selected, conforming to a total sample of 225 publications. The data were collected in an observation sheet in .xlxs format for analysis purposes. During the analysis process, including the discussion of possible discrepancies in interpreting each publication as belonging to one or another of the dimensions of the study’s codebook, the authors decided that each publication would be classified only in one dimension, depending on the type of content that predominates in each post.

To measure the level of reliability and accuracy of the instrument (Andrew et al., 2011 ), the intra-observer reliability method was applied, incorporating 10–12 minute breaks every 40–45 min of observation. After 15 days, the same publications were re-coded using the same established protocol. The results of the coding provided a Kappa coefficient of 0.949, demonstrating a very high level of agreement and reliability, following the scale of Landis and Koch ( 1977 ).

To measure the reliability and accuracy of the instrument (Andrew et al. 2011 ), the intra-observer reliability method was applied. In the first stage, the data was collected and coded post-by-post by applying the xlsx. sheet, incorporating 10–12 minute breaks every 40–45 min of observation to ensure the quality of the data observed and collected. The same posts were re-coded using the same established protocol in the second stage. To ensure a more accurate application of the codebook and to avoid potential bias, a 15-day impasse was established between the two data collections. The coding results between the two stages provided a Kappa coefficient of 0.949, demonstrating a very high level of agreement and reliability, following the scale of Landis and Koch ( 1977 ).

Finally, based on the interaction data collected with the data collection instrument, the variable of engagement with the publications was calculated by adapting the formulas used by the Fanpage Karma ( 2022 ) and Rival IQ (Feehan, 2023 ) platforms (Fig. 1 ).

Adapted from Fanpage Karma ( 2022 ) and Rival IQ (Feehan, 2023 ) platforms.

Therefore, after the protocol and the .xlsx observation instrument sheet were tested and validated, the final procedure was established as follows: (a) social media posts from Facebook, Twitter and Instagram of the selected football clubs were extracted automatically using the FanPage Karma license and added to the .xlsx observation instrument sheet; (b) according to the Study Codebook (see Table 2 ) the data was collected and registered manually into the .xlsx observation instrument sheet by clicking the posts one by one; c) we proceeded to set up a database coding the variables from the data collected to perform the statistical analyses.

Data analysis

A descriptive analysis of the engagement generated by publications on social media and their content (dimensions and formats) on Facebook, Instagram and Twitter was carried out. To analyse the differences in engagement generated by the posts on each social media according to their content, we used the t-test for independent samples and the one-factor ANOVA. The significance value established is <0.05. A chi-square test and correspondence analysis were applied to identify and visualise points of association between the key variables. Data analysis was performed using the SPSS statistical package, version 27.0.

As shown in Table 3 , of the 19,745 posts observed and analysed, Twitter accounted for 64%, followed by Facebook at 22% and Instagram at 14%. However, from the point of view of engagement, Instagram reflects an average of 1.873, well above the other social media. Facebook follows it with 0.112 and Twitter with 0.045, showing an inverse behaviour to the number of posts made.

Frequency and engagement

In Fig. 2 , we can observe the strategy used by each club in terms of the frequency of posts on Facebook, Twitter and Instagram, as well as the levels of engagement obtained. On Facebook, the football clubs analysed posts at different frequencies. In Europe, we observe that the clubs with the highest frequency of posts are Liverpool FC and Manchester United FC, with n  = 445 and n  = 486, respectively. In contrast, the Spanish clubs (Real Madrid FC and FC Barcelona) have the lowest frequency of posts ( n  = 195 and n  = 118, respectively). On the other hand, beyond this difference in frequency, they have very similar engagement ratios.

Frequency of posts and level of engagement generated on Facebook, Twitter and Instagram by the football clubs selected for this study (organised by regions).

The club with the highest frequency of publications is CR Flamengo from Brazil ( n  = 644); however, SE Palmeiras, the other Brazilian club studied, despite registering fewer publications in the same period ( n  = 289), shows much higher levels of engagement. SE Palmeiras (Brazil), Club Olimpia and Club Cerro Porteño (Paraguay), CF America (Mexico) and Atlanta United FC (USA) show the highest levels of engagement, with similar posting frequencies (between n  = 142 and n  = 241). On Twitter, the highest frequencies of posts were published compared to Facebook and Instagram, with CR Flamengo and Atlanta United FC being the clubs that posted the most ( n  = 1606 and n  = 2096, respectively). However, the levels of engagement identified show similar and homogeneous levels in the period analysed, regardless of the frequency of publications. On the other hand, the highest engagement levels were observed on Instagram, with a lower frequency of publications in all cases. Football clubs SE Palmeiras, CA River Plate, CF America and Atlanta United FC have the highest engagement values (2.5 and 3), with posting frequencies ranging from n  = 91 to n  = 154. European football clubs have very similar engagement ratios (around 1.00), while North American football clubs have different engagement values despite having similar posting frequencies ( n  = 91 and n  = 154).

Content dimensions of publications

As shown in Fig. 3 , we observe the dimensions proposed in this study, comparing the social media analysed and the engagement generated by each category. From this point of view, in terms of frequency, the “Marketing” and “Sport” dimensions are observed as the most used publication approaches by football clubs, followed by the “Institutional” dimension, “Commercial” and, finally, “ESG”. This order of frequency applies to Facebook, Twitter and Instagram.

Categorisation in the posts’ dimensions and their relationship with the engagement generated by Facebook, Twitter and Instagram of the football clubs analysed.

In terms of engagement, the social media Instagram is the one that registers considerably higher values than the rest of the social media analysed, with the “Marketing” dimension generating the highest engagement (2.03). It is followed by the “Institutional” dimension (1.78) and the “Sports” dimension (1.74), closing with the “Commercial” and “ESG” dimensions, with values of 1.54 and 1.41, respectively. Facebook is the following social media that generates the highest engagement.

In the case of Facebook (see Supplementary Table S1 ), the findings show a significance of the engagement means between the “Commercial” and the “Sports” ( p  = 0.000 < 0.05), “Institutional” ( p  = 0.001 < 0.05) and “Marketing” type of the posts in Facebook.

On the other hand, Twitter (see Supplementary Table S2 ) is the one that generates the minor engagement, with very similar values between the different dimensions, despite being the one with the highest frequency of publications (Fig. 3 ). Unlike the previous dimensions, the “Institutional”, “ESG”, and “Commercial” dimensions are those with the highest engagement values (0.07), followed by the “Marketing” and “Sports” dimensions (both with 0.04). However, in this social media platform, the “Institutional” type of content is statistically significant with “Sports” ( p  = 0.000 < 0.05), “Commercial” ( p  = 0.000 < 0.05) and “Marketing” ( p  = 0.000 < 0.05). Also, we can find significant engagement results between the “ESG” and the “Commercial” ( p  = 0.033 < 0.05) dimensions.

On Instagram (see Supplementary Table S3 ), the “Marketing” dimension has the highest engagement value, as does the “Institutional” dimension (both with 0.12). It is followed by the “Sports” dimension (0.11), “ESG” (0.10) and finally, “Commercial” (0.07) (Fig. 3 ). Nevertheless, as difference of Facebook and Twitter, the findings show a strong relevance of “Marketing” dimensions posts (Supplementary Table S3 ), linked significantly with “Sports” ( p  = 0.000 < 0.05), “Commercial” ( p  = 0.000 < 0.05) and “Institutional” ( p  = 0.002 < 0.05).

Types of formats in publications

Nine combinations of the most relevant formats have been identified in the publications analysed (Table 4 ), both in the frequency of use and engagement they generate.

On Facebook, the most frequent formats are “Text/Image” and “Text/Video” ( n  = 2031 and n  = 1265, respectively). However, the format with the highest engagement is “Image” (0.23), followed by “Text/Image” (0.13), “Text/Video” (0.12) and “Text/Link” (0.07). On Twitter, on the other hand, the “Text/Image” format is the most used ( n  = 4412), “Text” ( n  = 2499), “Text/Video” ( n  = 2239) and “Image” ( n  = 1534), with the “Text/Video” and “Text/Image” format combinations (0.07) registering the highest engagement. On Instagram, due to the nature of social media, the most frequent format is “Text/Image” ( n  = 1986). In terms of engagement, the formats “Image” (2.20), “Text/Image” (1.95), “Text/Image/Polls” (1.93) and “Video” (1.84) have the highest values.

The correspondence analysis (Fig. 4 ) shows the degree of association between the variables and the categorisation dimensions proposed in this study in a relative position map. The chi-squared test yielded a result of 1027.65. The “Marketing” dimension shows a closer relationship with the “video” and “image” format resources. The “ESG” and “Institutional” content type shows an association with the “Image” and “Text” formats. The “Commercial” dimension, based on the characteristics of the categorisation, shows a relationship with the “Link” format as ideal points of association, considering the frequency and engagement analysed.

Correspondence analysis (dimensions and formats).

Nowadays, sports organisations and athletes use social media for communication purposes, brand positioning, visibility (Maderer et al., 2018 ; Winand et al., 2019 ; Zakerian et al., 2022 ) and even for potential business (Parganas and Anagnostopoulos, 2015 ), dedicating effort and resources. Previous studies reinforce the need to categorise the message delivered to understand this phenomenon according to the objective (Filo et al., 2015 ) and content analysis for effect (Meng et al., 2015 ). However, its optimal use still leaves many questions. The complexity of the market is evolving towards the need to understand the fan as a premise in a sector characterised by its high emotional charge. In the past, strategies focused on attracting and retaining fans. However, the current trend shows increased relevance in generating engagement (Oviedo et al., 2014 ) to generate links with fans. The sports industry, especially in the digital environment, is in an era where the goal is not just getting new followers and post social media content but interact and engage “to know the users better”.

First, this study provides evidence of relevant frequency-engagement relationships according to the dimensions of the study, depending on the type of social media used (Facebook, Twitter and Instagram). Regarding the dimensions of the content published, the posts related to “Marketing” and “Sport” are the most frequent due to the natural and traditional use of these tools as communicative, brand positioning and informative elements (Lee and Kahle, 2016 ; Rehman et al., 2022 ; Winand et al., 2019 ). This is attributable to the need for clubs to generate emotional content (such as videos or images of past iconic matches or campaigns involving athletes), on the one hand, and to broadcast messages alluding to sporting performance and results. Nevertheless, the findings show different engagement impacts not directly linked to the frequency of the posts but influenced by other elements, such as the social media platform, the dimension of the content and the format. The evidence shows there are specific content dimensions that statistically generate more engagement in each platform.

On Facebook, the most traditional platform football clubs use provides a more balanced frequency-engagement ratio, with a strong engagement with “commercial” content. This platform was one of the social media platforms that started monetising in other industries, characterised for its high brand impact, where the know-how and the platform interphase are more friendly to focus on this type of posts (and in some cases, to launch joint posts with brands). Even with the positive engagement impact of this platform, it is observed that efforts of this nature in the digital sphere are scarce in comparison to the rest, making this a relevant aspect in the spectrum of growth and an opportunity to explore, especially with the new assets that are appearing in the market and the growth of e-commerce.

On Twitter, on the other hand, the dimension that works best for engaging in “Institutional” is linked to “Sports”, “Marketing” and “Commercial” content, but not with “ESG”. However, the “ESG” linked with “Commercial” dimensions statically gets significantly more impact on this platform. The “ESG” dimension is emerging as this platform is used for promoting socio-political activities and promoting more altruistic purposes as previous authors as López-Carril and Anagnostopoulos ( 2020 ), and Sharpe et al. ( 2020 ) noted. This strategy shows a possible intention to use social media not only for marketing (communication) or sporting purposes but also as an element with socio-political aspects. The nature of Twitter as a microblogging site with the highest number of posts with the lower means of engagement, is more attractive for the audience looking for quick and summarised information because of its ability to increase the visibility and awareness of fans (Abeza et al., 2017 ). Sports managers can focus on this type of message for a potential higher engagement on Twitter.

In contrast, on Instagram, the focus is on “Marketing” content. This platform shows the lowest number of post frequency, with a high engagement means, attributable to the platform’s audio–visual formats and more interactive content, ratifying its growing popularity among users. As a fast-growing platform, there is a major link with “Sports”, “Institutional” and “Commercial” dimensions, which makes it an ideal platform for emotional content, easy to connect with brands, athletes, and sports properties, counting with a larger and more varied audience looking mainly, as the evidence suggests, for entertainment and club’s closeness perception. Therefore, like Anagnostopoulos et al. ( 2018 ), we recommend sports managers use Instagram for marketing purposes, considering the context as a relevant factor.

Finally, this study reveals the post format’s relevance as another key element. In this sense, on Facebook, the highest engagement values are generated by “Image” and “Text/Image” formats, as on Instagram and Twitter; however, in each social media platform, the frequencies generated by these records are different. In any case, the power of the image as valuable content in marketing stands out, as it has also been highlighted in previous studies (e.g., Anagnostopoulos et al., 2018 ; Doyle et al., 2022 ; Machado et al., 2020 ). Nevertheless, the results obtained regarding the engagement triggered by video format posts on Facebook, Twitter and Instagram are not as conclusive, as other studies have pointed out (e.g., Su et al., 2020 ). Probably because these social media are not focused on that format as other social media such as TikTok or YouTube may be. Regardless, based on the results obtained, it is necessary for sports managers and academics to continue to explore and make the appropriate combinations of the dimensions of content type categorised in this study, the publication format, as well as the social media used to channel them.

Theoretical implications

Built upon the framework of relationship marketing, this study brings theoretical value to the realms of sports marketing, sports management, and fan engagement, spanning across four distinct lines of action.

Firstly, the research introduces a novel theoretical approach to social media strategies by employing a 5-dimensional content categorisation system aligned with the strategic pillars of football organisations. Previous studies have predominantly approached the role of social media in sports reactively, primarily focusing on communication and branding aspects. In contrast, this study contributes to the literature by adopting a strategic perspective towards social media, establishing a linkage between the study dimensions and football club strategies. This foundation paves the way for future research to delve deeper into each proposed dimension, potentially identifying sub-groups and exploring them in greater detail. The proposed dimensions serve to systematically organise the primary facets of football organisations for digital context analysis, a realm of increasing importance within the sports industry. As such, this work marks a pioneering step towards a novel approach in this area of study.

Secondly, this study establishes a fresh frequency-engagement approach for social network management, dispelling the notion that post frequency directly correlates with generated engagement. In doing so, this work highlights additional pivotal factors beyond post frequency that influence engagement among users of football-related social media. This perspective is aligned with the ethos of Web 2.0, underscoring the significance of engaging and connecting with fans.

Thirdly, from a theoretical perspective, this study introduces an innovative analytical proposition focusing on prominent international football clubs. This innovation is realised through the calculation and translation of engagement ratios, facilitating cross-entity comparisons independent of geographical location and follower count. The instrument developed and applied in this study acts as a tool to identify valuable digital practices within the industry.

Finally, this study stands out by conducting simultaneous analyses of posts across three prominent social media platforms (Facebook, Twitter, and Instagram), adopting a distinctive multi-platform approach that is seldom observed in comparable studies which often focus on a single social media platform. Gaining insights into the effects of cross-platform and cross-format postings can empower sports managers to make strategic decisions with a comprehensive perspective.

Practical implications

This study introduces a novel practical tool designed for the computation of fan engagement across the Facebook, Twitter, and Instagram accounts of football clubs globally. Consequently, sports managers can employ this instrument to gain a more realistic comprehension of the performance of social media accounts belonging to clubs. Furthermore, the developed tool facilitates the assessment of fan engagement in relation to the content type being published. This capability can aid sports managers in fortifying the bond between clubs and their followers by generating heightened value through strategic social media initiatives.

It is important to note that sports managers should consider both internal factors (club tradition, organisational culture) and external factors (competition, fan behaviour, sports results) within the context of clubs. This consideration is essential for developing and planning optimal digital strategies and for generating the best possible engagement with the audience. This research furnishes empirical evidence for understanding, in a practical and actionable manner, the pivotal components of a social media post. This understanding permits the visualisation of optimal combinations of these elements, thereby increasing the likelihood of sports managers guiding the club toward success and fostering substantial user engagement. Therefore, football team managers can apply the findings of this study to plan, monitor, and evaluate the club’s social media content for increased engagement and “closeness” with digital fans. They can combine various formats based on individual post requirements to achieve the desired results. Additionally, football team managers can analyse club identity and overall strategies more practically and coherently, facilitating the planning and execution of more effective commercial, brand positioning, institutional, and other relevant digital goals, with engagement serving as a key metric.

Conclusions

Social media plays a key role in today’s sports management, especially in football clubs, due to its global reach and ability to interact and connect with fans in an industry of great popularity, emotional charge, and economic, political and social impact. This exploratory research grounded in relationship marketing theory provided a comparison of the engagement generated by elite football clubs under a unique categorisation proposal, derived and adapted from existing literature, which addresses dimensions linked to strategic areas of football organisations and takes into consideration key elements such as frequency and format combinations used to analyse the efficiency of posts on Facebook, Twitter and Instagram.

Based on the results obtained, three lines of action stand out. First, concerning the type of content of the post, the “Marketing” and “Sports” dimensions are the preferred categories for football clubs in terms of post frequency. Regarding the engagement rates, on Facebook, the “Commercial” dimension shows an opportunity for growth and development due to the good engagement impact and due to the technological boom and the emergence of new digital assets. On Twitter, the emerging “ESG” linked to “Commercial” perspective and the “Institutional” dimension gets a significant impact on Twitter. On Instagram, the “Marketing” dimension linked to “Sports”, “Institutional” and “Commercial”, makes this platform ideal for emotional and marketing purposes. Second, concerning social media sources, this study provides evidence that Instagram is the social media that generates the most engagement using the lowest frequency of posts, followed by Facebook and Twitter. There is no direct evidence that links the post’s frequency with the engagement generated. Finally, concerning the type of format of the post, the combination of formats that generates the most engagement in all cases is “Image”, “Text/Image”, and “Text/Video”.

In short, this research stimulates a practical reflection for professionals and academics on the exploration, analysis, and evaluation of the management of social media in football clubs, using the observation method and content analysis techniques, applying elements of reliability and scientific rigour. The results obtained in this study offer practical and managerial implications in sports management, fan engagement, digital marketing, and social media, among others, through a proposal for categorisation and unique variables, taking engagement and its influence within the context of analysis as the axis.

The above conclusions should be taken into consideration viewing a series of limitations of the study. Firstly, the sample is limited to one sport (football) and not a large number of football clubs from different regions of the world. Secondly, despite the high number of posts analysed, these are located over a short period of time, and it may be relevant to analyse the engagement of posts at different times of the season, as these can influence the type of content and the engagement of fans with the posts. Thirdly, the study is limited to analysing engagement on Facebook, Twitter and Instagram, leaving aside the analysis of the possibilities that other booming social media, such as TikTok or Twitch, are having in the field of marketing. Nevertheless, these limitations can be a starting point for future research lines including, among others: (a) to assess the application and feasibility of the technique for measuring social media engagement included in this work in other football organisations (e.g. leagues) or social media platforms (e.g., TikTok, Twitch); (b) to incorporate new variables of study (e.g., size of the social mass of sports clubs, financial budget, trophies won); (c) to conduct the study considering different phases of the sports season (e.g.; preseason, season, playoffs; postseason); (d) to analyse fan engagement relation of geographical regions to understand the digital user’s behaviours; (e) to conduct the study adding engagement prediction models in social media; and (f) to incorporate this model on an AI language to suggest and predict digital user engagement in a simulated context.

Data availability

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to acknowledge the experts who contributed their excellent technical knowledge and valuable inputs to the development of this work and the Fanpage Karma platform for providing the software licence to support this research. Edgar Romero-Jara would like to acknowledge the funding support of the pre-doctoral scholarship “National Academic Excellence Scholarship Programme Carlos Antonio López (BECAL)”, granted by the Government of Paraguay. Samuel López-Carril would like to acknowledge the funding support of the postdoctoral contract “Juan de la Cierva-formación 2021” (FJC2021-0477779-I), granted by the Spanish Ministry of Science and Innovation and by the European Union through the NextGenerationEU Funds (Plan de Recuperación, Transformación y Resilencia).

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ER-J (corresponding author) and FS: conception and design of the work. ER-J and JM: analysis and methodology. ER-J and SL-C: literature review, interpretation of data, drafting of the work. FS: supervised this work. All authors made substantial contributions, discussed the results, revised critically for important intellectual content, and approved the final version of the work.

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Romero-Jara, E., Solanellas, F., Muñoz, J. et al. Connecting with fans in the digital age: an exploratory and comparative analysis of social media management in top football clubs. Humanit Soc Sci Commun 10 , 858 (2023). https://doi.org/10.1057/s41599-023-02357-8

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Understanding How Digital Media Affects Child Development

Technology and digital media have become ubiquitous parts of our daily lives. Screen time among children and adolescents was high before COVID-19 emerged, and it has further risen during the pandemic, thanks in part to the lack of in-person interactions.  

In this increasingly digital world, we must strive to better understand how technology and media affect development, health outcomes, and interpersonal relationships. In fact, the fiscal year 2023 federal budget sets aside no less than $15 million within NICHD’s appropriation to investigate the effects of technology use and media consumption on infant, child, and adolescent development.

Parents may not closely oversee their children’s media use, especially as children gain independence. However, many scientific studies of child and adolescent media use have relied on parents’ recollections of how much time the children spent in front of a screen. By using software embedded within mobile devices to calculate children’s actual use, NICHD-supported researchers found that parent reports were inaccurate more often than they were on target. A little more than one-third of parents in the study underestimated their children’s usage, and nearly the same proportion overestimated it. With a recent grant award from NICHD, researchers at Baylor College of Medicine plan to overcome the limitation of relying on parental reports by using a novel technology to objectively monitor preschool-age children’s digital media use. They ultimately aim to identify the short- and long-term influences of technology and digital media use on children’s executive functioning, sleep patterns, and weight. This is one of three multi-project program grants awarded in response to NICHD’s recent funding opportunity announcement inviting proposals to examine how digital media exposure and use impact developmental trajectories and health outcomes in early childhood or adolescence. Another grant supports research to characterize the context, content, and use of digital media among children ages 1 to 8 years and to examine associations with the development of emotional regulation and social competence. A third research program seeks to better characterize the complex relationships between social media content, behaviors, brain activity, health, and well-being during adolescence.

I look forward to the findings from these ongoing projects and other studies that promise to inform guidance for technology and media use among children and adolescents. Additionally, the set-aside funding for the current fiscal year will allow us to further expand research in this area. These efforts will help us advance toward our aspirational goal to discover how technology exposure and media use affect developmental trajectories, health outcomes, and parent-child interactions.

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DC Metropolitan Police Department Social Media Monitoring Documents

A lawsuit by the Brennan Center and Data for Black Lives unearthed documents about how the DC police department monitors social media.

• Social Media

On December 15, 2020, the Brennan Center for Justice and Data for Black Lives (D4BL) submitted a public records request to the Washington, DC, Metropolitan Police Department (MPD) for information on how the department uses social media to collect information about individuals, groups, and First Amendment–protected activities. The Brennan Center and D4BL also submitted a request to the DC Office of Contracting and Procurement (OCP) on March 17, 2021, seeking information about vendors with which the district has contracted to collect information from social media.

As the Brennan Center has repeatedly warned , law enforcement agencies across the country gather information from social media platforms like Facebook, Twitter (now X), and Instagram in ways that disparately harm communities of color and often infringe upon constitutionally protected speech. In the district, previous reporting indicated that the MPD has conducted broad monitoring of First Amendment–protected activity, including Black Lives Matter demonstrations and protests against police brutality during the summer of 2020. But members of the public lack an adequate understanding of how law enforcement agencies use social media to monitor, track, and investigate them online. This public records request is a part of the Brennan Center’s series of public records requests to police departments nationwide to shed light on this surveillance.  

After the MPD produced only a handful of documents, we submitted an appeal to the DC Mayor’s Office of Legal Counsel on December 22, 2021, challenging the adequacy of the district’s response to our request. The Brennan Center and D4BL, represented pro bono by Ballard Spahr LLP, sued the District of Columbia on March 1, 2022, to obtain the documents to which we were legally entitled. On October 13, 2022, we submitted an additional public records request for documents related to the MPD’s use of undercover social media accounts.

• Read the MPD public records request here .
• Read the OCP public records request here .
• Read the undercover accounts records request here .
• Read the administrative appeal here .
• Read the Brennan Center’s and D4BL’s complaint here .
• Read the Brennan Center’s and D4BL’s motion for partial summary judgment here .
• Read the Brennan Center’s and D4BL’s motion for summary judgment here .

Overall, we obtained over 160,000 documents, totaling almost 750,000 pages. The documents that the MPD produced fall into five broad categories: monitoring of protests and assemblies, monitoring of communities of color, undercover social media activity, documents related to MPD criminal research specialists, and engagement with vendors of social media monitoring tools.

Monitoring of Protests and Assemblies

Email communications.

The Brennan Center and D4BL obtained emails between the MPD and government agencies at both the federal and local levels exchanging information about protests in the summer of 2020. We also obtained similar communications from 2015 wherein MPD officers shared posts from Facebook and Twitter about protests after Freddie Gray died in police custody in Baltimore. Throughout the communications from 2020, representatives from the MPD and other law enforcement agencies exchanged raw, seemingly unverified information (often sourced from social media) and speculated about Antifa (anti-fascist) involvement in the protests.

On August 31, 2020, for example, a Secret Service officer within the national Joint Terrorism Task Force asked another Secret Service officer in the Washington Field Office to corroborate the MPD’s claims that “a portion of protesters were ‘known Antifa members’ ” who were “throwing objects at police” and stretching “rope . . . across the street in an attempt to trip officers.” The Task Force officer noted that the Secret Service had not provided evidence of Antifa support in the crowd. When the Washington Field Office Secret Service officer asked the MPD to confirm these findings, a lieutenant within the MPD’s Homeland Security Bureau observed that “two known Antifa members” whose names are redacted “were within the group and participating in marches” without identifying additional evidence of any threat they posed.

A Capitol Police employee also singled out one Twitter user who “was posting photos and videos from the protests” as being “one of the main Antifa organizers in DC.” In another email to representatives from the MPD and federal agencies, from August 28, 2020, a Capitol Police employee flagged a post from a Twitter user — seemingly the same woman described as an Antifa organizer — announcing that she had given birth to her second child. The employee noted that “this certainly doesn’t mean she’s not out and about in the protests,” since she had “posted pictures of her older child sleeping in the backseat of a car” at another protest.

These communications continued well into the following year. On March 13, 2021, an MPD Homeland Security Bureau lieutenant sent representatives from the Secret Service and the Capitol Police an email flagging two vigils for Breonna Taylor (one of which was organized by a teen activist group), along with links to Instagram posts containing information about the vigils. Even though the lieutenant observed that the organizers of these events had previously held peaceful protests without incident, they noted that another organizer “not known to MPD” had called for “people to dress in ‘black bloc,’ indicating possible civil disobedience and/or criminal activity.” In response, an assistant commander within the U.S. Park Police’s Intelligence and Counterterrorism Branch speculated, with no apparent evidence, that the organizer was “some kind of online instigator from out of the area.”

Demonstration Reports

We obtained documents compiling information about upcoming assemblies and demonstrations from February 2020 to January 2023, much of it drawn from social media. For example, one April 2021 demonstration report lists details about police brutality protests, May Day demonstrations, and assemblies regarding conflicts abroad, many of which include information sourced from social media. For each protest, the reports include the time and location of the event, the purpose, the estimated number of participants, the source of the information, and, occasionally, information about the organizer of the event. Additionally, we obtained emails establishing that the MPD sends at least some of these demonstration reports to other local, state, and federal government agencies as well as some private companies. For instance, the MPD sent its June 17, 2020, demonstration report to entities including Immigration and Customs Enforcement, the Department of Defense, Montgomery County in Maryland, and Amazon.

We also obtained documents and emails from other federal and local law enforcement agencies that compile information about planned protests. Two reports from June 2020 sent to the MPD by the Department of Transportation and the Naval Criminal Investigation Service include a section listing tactics, techniques, and procedures that the national Joint Terrorism Task Force determined were used by racial justice protesters, including “prestaging of bricks, rocks, sledge hammers [ sic ], . . . and other weapons at protest locations.” Contemporaneous reporting indicated that there was no evidence that protesters were assembling stacks of bricks to use as weapons, but claims like these were nevertheless widely disseminated by law enforcement agencies nationwide.

Monitoring of Communities of Color

The Brennan Center and D4BL obtained documents about the MPD’s Summer Crime Prevention Initiative (SCI), an effort that uses law enforcement personnel and technology to intensively patrol four to six areas that the department determines have a high density of violent crime. The SCI has been in operation since 2010 and as of 2019 expanded to a Fall Crime Prevention Initiative. Our findings are consistent with public reporting indicating that the SCI involves intensely patrolling Black and brown areas of the district to identify and monitor purported gang members.

We obtained a memorandum from April 20, 2011, that tasked the MPD’s Criminal Intelligence Branch (CIB) with creating teams to monitor social media for information on criminal activity. The memo states that “members shall continually monitor open pages that may have ties to known gang areas,” though there is no definition or explanation of what “may have ties” means. Team members are also directed to search through social media sites to uncover relevant information about violent incidents, including gang rivalries. Additionally, if team members suspect that a page on social media that “requires an invitation” contains “information concerning criminal activity and criminal associations,” they may seek approval from the CIB lieutenant to access those pages, likely using a covert social media account. Team members may also seek approval to interact with people online. It is unclear whether this memorandum remains operative.

We also obtained heavily redacted SCI Area Enforcement reports from May to July 2014 containing information about four designated areas or groups — Benning Corridor, Choppa City, Barry Farm, and Washington Highlands, which are in overwhelmingly Black wards of DC — sourced almost entirely from Twitter. Though each report states that it contains information found on social media “pertaining to ongoing criminal activities, beefs, and retaliations,” the little information that the MPD left unredacted demonstrates that these reports also include events and gatherings that appear far more innocuous, such as a birthday party, a graduation celebration, a cookout , a trip to Six Flags , a mixtape release party , and a concert.

The MPD did not produce reports for the Summer Crime Prevention Initiative held in other years and provided none for the Fall Crime Prevention Initiative, and it is unclear whether the MPD has continued assembling similar reports using information from social media.

Undercover Social Media Accounts

According to the MPD’s November 2021 policy governing the use of social media for investigative and intelligence-gathering purposes, undercover social media accounts may be used only in certain (undefined) circumstances by members of five divisions: Criminal Investigations, Intelligence, Internal Affairs, Narcotics and Special Investigations (NSID), and Youth and Family Services. Members of these divisions must obtain written approval from the NSID commander prior to using or creating an undercover account, and commanding officers are directed to monitor their members’ use of undercover accounts, conducting a documented review every 30 days.

Through our request targeting the use of undercover social media accounts, the Brennan Center and D4BL obtained a heavily redacted social media username log listing eight undercover social media accounts that were approved by the NSID commander. Seven of these, including one that was “de-listed” in April 2022 and “used for monitoring only,” are undercover accounts on Instagram, and one account is for Facebook. Undercover accounts violate Facebook’s platform policy , as both Facebook and its parent company, Meta, have repeatedly told law enforcement agencies. It is also likely that the MPD uses assumed personas to conceal these accounts’ law enforcement affiliation, implicating Instagram’s policy against creating accounts for the purpose of misleading other users.  

We also obtained 11 monthly reports dated between February and December 2022 that contain information about activities conducted using the undercover social media accounts overseen by the Violent Crime and Suppression Unit, which appears to be the successor to the NSID. Only one undercover account was used during that entire period as part of an investigation, which occurred in January 2022, according to the reports. The reports also state that undercover accounts not being used as part of “specific investigations are maintained for overt monitoring,” which typically means using fake accounts to monitor information online without communicating with individuals. These activities are not documented in the reports, likely inhibiting robust oversight. Accounts can also be maintained for “potential future undercover needs.”

Last, we obtained an undated presentation for the Gun Recovery Unit titled “Social Media Investigations,” which includes a heavily redacted section on undercover uses of social media — activities that the presentation acknowledges “typically violate the User Agreement from social media platforms.” According to the presentation, “covert accounts” are “used for focused investigations” and involve communicating with people online. According to the presentation, officers need a “ starting point ” (i.e., a reason) to communicate with someone online using a covert account, including tips from citizens or officers and information uncovered using social media aggregators, “hashtags or names from your experience as a law enforcement officer,” “Gang Books,” and MPD databases.

Criminal Research Specialist Documents

Agency guidance.

The Brennan Center and D4BL obtained two policies governing how criminal research specialists within the MPD’s Investigative Support Section (formerly the Investigative Support Unit) conduct research to support investigators and detectives working in the field, including on social media. First, the Social Media Use Policy dated December 19, 2014, governs how specialists use social media as part of their duties. The policy permits specialists to seek and store public information on social media using department–established accounts only in three scenarios: “based upon a criminal predicate or public safety threat”; to assist in investigations, prosecutions, “justice system response[s],” and crime prevention; and when the information is useful for “crime analysis or situational awareness reports.”

Second, the Execution of Social Media Searches policy dated February 6, 2018, guides how specialists may search for information on social media. At a minimum, specialists are required to query multiple combinations of a subject’s name, phone number, and email address on Facebook, Google, and at least two additional search engines listed in the Investigative Support Section Online Resources document , which includes several websites that compile information on individuals’ phone numbers, emails, social media accounts, and more. The resource document also includes a list of various social media platforms, including some sites used primarily by communities of color, such as Black Planet and MiGente (now defunct). Specialists are also required to run, for each subject, an Accurint Virtual Identity Report — a service provided by data broker, LexisNexis, that compiles individuals’ personal information — and to consult each page provided in the subject’s report. If the subject is a minor or the specialist cannot find any public information on the subject, specialists are directed to consult their relatives’ lists of connections on social media to uncover the subject’s social media profile.

We also obtained an undated presentation provided to the Investigative Support Section titled “Social Media” that includes various case studies to teach specialists how to search through social media according to the procedures in the Execution of Social Media Searches policy. Though the presentation is heavily redacted, it appears to focus in part on using social media to identify or monitor gang members. For instance, one slide directs specialists to proactively “check in on known recidivists and gang/crew members” who have a “social media footprint.” Another slide appears to direct officers to search through Instagram to determine whether a subject of an investigation is affiliated with a crew.

Social Media Search Logs

The records also include a 265-page log documenting criminal research specialists’ social media searches from November 2013 to January 2023, revealing how specialists comb through Facebook , YouTube , and Twitter profiles to investigate crimes. For example, an officer searched through a person’s Twitter and Facebook photos to note that they included “gang signs” and “marijuana.” Officers also watch peoples’ music videos or listen to their Sound Cloud to dig up information, or in some instances trying to identify people featured in YouTube videos with no explanation as to why. The police also rely on Morpho Face , a facial recognition tool, and the department’s oft-criticized gang database . The records reflect how people who are not suspected of crimes, as well as relatives , girlfriends , and associates of those involved, may be swept into the MPD’s surveillance. For example, an officer ran social media searches for a person’s “mother, father, godmother, other relatives,” while another search yielded “Facebook, Twitter, [Instagram,] and YouTube accounts for [a person of interest] and his mother.”

Social Media Monitoring Tools

Babel street.

BabelX is the flagship product of the social media monitoring company Babel Street, which, according to the company, collects information from dozens of social media platforms and across the internet, allowing its users to search for and analyze information in hundreds of languages. The DC fusion center has had access to Babel Street since at least August 2014, when the company conducted a demonstration of its social media monitoring capabilities.

Fusion centers are intelligence hubs developed in the aftermath of 9/11 to share counterterrorism information and criminal intelligence among local, state, and federal government entities and some private organizations. The district’s fusion center — formerly the Washington Regional Threat Analysis Center until it was renamed the National Capital Regional Threat Intelligence Consortium in 2018 — is overseen by DC’s Homeland Security and Emergency Management Agency (HSEMA).

In 2015, the fusion center created filters on BabelX to collect information from social media on behalf of the MPD, including posts related to homicides and threats against local law enforcement officers. Fusion center personnel reviewed this information before sharing the most relevant postings with the MPD. It appears that the MPD provided the fusion center with  search terms. For example, the assistant chief who managed the MPD’s Homeland Security Bureau stated that the department would provide the fusion center with a “generic glossary of the most common [slang] terms” to use on Babel Street. In another instance, the MPD provided Babel Street with keywords to search for information related to an event at Robert F. Kennedy Stadium that included terms related to both assemblies and potential threats, such as “protest,” “demonstration,” “rally,” “armed,” and “evacuation.”

In April 2015 Babel Street provided HSEMA with a spreadsheet compiling social media accounts that were “geo-located in both Ferguson [Missouri] and Baltimore during times of unrest” — presumably referring to the protests after the deaths of Michael Brown and Freddie Gray, respectively. Accounts to news organizations and journalists were excluded. The spreadsheet also contains a list of 58 social media users who Babel Street determined were “common connections” between the accounts in its first list — in other words, individuals or groups who did not necessarily have a connection to the protests but simply had a connection to those who did. Though HSEMA ultimately provided the  spreadsheet to the MPD, it is unclear whether either agency subjected the social media users included in the spreadsheet to further scrutiny.

We also obtained procurement documents from 2016–2020 showing that BabelX licenses were purchased for the DC fusion center, as well as a memorandum of understanding between the MPD and HSEMA that provides the police department access to the fusion center’s social media monitoring tools, which would include Babel Street. It is unclear whether the fusion center continues to have access to BabelX.

Dataminr, a company affiliated with Twitter, provides a FirstAlert tool that gives clients customized, real-time alerts about events uncovered through social media. Dataminr provided the MPD with 40 user licenses during a no-cost pilot in January and February 2017. During its trial, the MPD collected information from social media  related to events including “riots” during President Trump’s inauguration and during the first Women’s March. In February 2018, the MPD purchased seven annual Dataminr licenses at a cost of almost $48,000 using State Homeland Security Grant program funds from the Department of Homeland Security. According to internal communications, six of these licenses would be provided to the DC fusion center, and the MPD would have access to “unlimited licenses for the first year” of the contract, which it would provide to the department’s Command Information Center.

The Brennan Center and D4BL did not obtain procurement documents beyond the February 2018 purchase. However, it appears that the MPD lost access to Dataminr at some point before June 2020. In May 2020, Dataminr sent the MPD two promotional papers , including one in which it claimed that its FirstAlert tool had unearthed some of the very first indications of the Covid-19 pandemic in December 2019. Soon after, an MPD representative contacted the District’s Office of the Chief Technology Officer (OCTO), which paid about $200,000 for 50 Dataminr licenses, 45 of which it provided to HSEMA. On May 29, 2020, OCTO’s chief data officer responded to the MPD, stating that HSEMA could provide the MPD with some of its unused Dataminr licenses, which he said “would be very handy” for the MPD’s response to protests in the aftermath of George Floyd’s murder. The MPD gained access to Dataminr seemingly through an arrangement with HSEMA.

The Brennan Center and D4BL obtained more than 700,000 pages of email notifications from Dataminr First Alert to members of the MPD, dated between June 4, 2020, and May 20, 2022. In these emails, Dataminr sent the MPD information about protests, including real-time information about anticipated demonstrations, where protests were forming and moving , and protesters’ activities , often without any apparent connection to public safety.

Dataminr’s transmissions to the MPD belie the company’s position that it only provides news alerts to law enforcement and does not permit the use of its tool to surveil First Amendment–protected activity. The tension between Dataminr’s capabilities and its policy against surveillance was apparent in one December 2020 exchange between the company and the MPD when an MPD representative noted that Dataminr had failed to flag for the department “social media chatter” in the run-up to January 6, 2021. In response, one Dataminr employee stated that the company could not alert law enforcement about the “planning or scheduling of protests or demonstrations,” even though the company had provided this type of information to the MPD during the summer of 2020. The MPD replied that it was “concerned with the threats of ‘armed protesters’ and people planning on bringing firearms” to the district.

The MPD used Sprinklr on a trial basis from January to March 2017, a period that mostly coincided with the Dataminr trial. Like Dataminr, Sprinklr’s social media monitoring tool appears to rely on a user’s search terms to scour online platforms for relevant postings. During the trial, the MPD obtained six Sprinklr licenses that it distributed to the “POI team,” the Intelligence Branch, and the Fusion Desk. Though the trial was initially set to cost $40,000, we did not receive documents indicating whether the MPD paid for the trial.

The MPD’s Fusion Desk — a unit charged with providing “situational awareness and operational intelligence to MPD personnel” — used Sprinklr during former President Trump’s inauguration “to monitor key terms that could have an impact on” the district, “then fed this information to” other MPD divisions to “apprise them of real-time events as they unfolded.” The list of search terms that the MPD used on Sprinklr during the inauguration focused entirely on eliciting activity surrounding anti-Trump protests, including hashtags like #DisruptJ20, #RefuseFascism, #ResistTrump, #Anticapitalist, and #Antifa.

The MPD created two other lists of search terms for Sprinklr following the inauguration. The first was a “universal list” that included the search terms “Active Shooter,” “Evacuation,” “Protests,” “Terrorism/Terrorist,” and “ISIS.” The second list was limited to activity in the DC area and used search terms about a range of criminal activity, from “Graffiti” to “Stabbing” and “Shooting.” Internal emails also indicate that the MPD was considering assembling search queries for particular events in the district. For example, one MPD employee proposed collecting information about “A Day Without Immigrants” protests in February 2017 using search terms such as #BreakLunch and #GeneralStrike. It is unclear whether the MPD ultimately used Sprinklr for this purpose.

In December 2015, the MPD’s Intelligence Division conducted a two-week evaluation of VoyagerAnalytics , an “AI-based analysis platform” developed by a company called Voyager that collects information from social media (including Facebook, Twitter, and Instagram) and purportedly uncovers details about individuals’ “relationships, the strength of those relationships, the prominent topics and narratives important to him or her, as well as hundreds of other signals, allowing [users] to derive significant, actionable insights.” Following the evaluation , the Intelligence Division found that the tool “exceed[ed] the capabilities of all other social media investigative tools [the division] had tried.” In January 2016, Voyager provided the MPD with a pricing proposal of $37,000 to $60,000 per year for three users, though it appears the department ultimately did not move forward with the purchase.

The MPD trialed Voyager again the following year. It sought to use Voyager in January 2017 to monitor activity surrounding the presidential inauguration in a trial coinciding with its trials of Dataminr and Sprinklr. It appears the MPD ultimately began its trial with Voyager after June 2017. According to a memorandum requesting approval to use Voyager, the MPD sought to provide user licenses to criminal research specialists, members of the Criminal Intelligence Bureau, and personnel assigned to the Joint Terrorism Task Force. The memo highlights Voyager’s “integrated analytics” that allow users “to conduct network analysis and [uncover] in depth information from over six social media platforms from non-attributable proxy servers.” Though the MPD attempted to purchase three licenses for Voyager in 2017, each costing $30,000, the department ultimately did not move forward with the purchase because of the cost.

In 2019 the MPD requested another pricing proposal from Voyager as it prepared to apply for grant funding, stating that the department would request licenses for up to 25 users, including personnel from the gang unit. Though Voyager conducted a demonstration with the MPD to showcase its tool’s updated capabilities, it is unclear from the documents whether the department moved forward with purchasing licenses for Voyager.

Data Brokers

The Brennan Center and D4BL also obtained documents demonstrating that the MPD contracted or had trials with three companies — Transunion, LexisNexis, and Thomson Reuters — for data brokerage services, which included access to information from social media.

Since at least 2015, the MPD has contracted with Transunion for access to its database service, TLOxp, which contains hundreds of millions of data points of individuals’ sensitive information including social media, vehicle tracking data using license plate readers, utility data, and more. In a marketing email from May 2019, Transunion advertised TLOxp’s Social Media Comprehensive Report feature, through which users “could reveal more about a subject’s digital identity through information not readily accessible via other forms of public records data.”

According to an email thread from August 2018, TLOxp was most used by the MPD’s criminal research specialists who conduct research to support officers working in the field; it was also used for broader crime analysis and situational awareness within the department. The specialists used TLOxp to obtain information about individuals’ phone numbers, home addresses, and social media activity.

Though we obtained an email thread from April 2022 showing that the MPD was considering renewing its access to TLOxp for the 2023 fiscal year, it is unclear whether the department continues to have access to the database. However, we also obtained dozens of emails notifying MPD employees that their TLOxp accounts had been inactive for 30 days or responding to a request to reset accounts’ passwords, demonstrating that some MPD employees had TLOxp accounts until at least June 4, 2022.

We also obtained email communications indicating that MPD accessed LexisNexis’s social media monitoring services in 2014 through Accurint, the company’s flagship database. While the MPD’s Intelligence Branch held a trial of LexisNexis’s social media monitoring services in March and April 2014, a LexisNexis representative provided the MPD with a proposal to increase the department’s existing contracts for Accurint from 35 users to 50 users and to incorporate Accurint’s social media monitoring services starting in May 2014. The total cost of the contract would increase from $39,348 to $82,150 per year. Instead, the MPD opted to pay to extend the Intelligence Branch’s trial from May to at least November 2014 and did not incorporate social media monitoring features into its annual license renewal for fiscal year 2015. It is unclear whether the department eventually purchased annual licenses for LexisNexis’s social media monitoring services.

Last, we obtained email communications indicating that the MPD conducted a 14-day trial of CLEAR, the data brokerage service from Thomson Reuters, in May 2014, as well as a demonstration of CLEAR’s “Social Media Threat Tool.” However, it appears that the MPD did not move forward with purchasing access to CLEAR because officers did not find it useful. In 2020 the MPD’s Homicide Branch conducted another trial of CLEAR, after which Thomson Reuters submitted a proposal to the department that would cost between $112,100 and $450,000 annually, depending on the number of users. It is unclear whether the MPD moved forward with purchasing licenses for CLEAR.

Compiled Production Documents

Original productions.

March 2021 OCP Production

September 2021 MPD Production

October 2021 OCP Production

June 2022 MPD Production

First September 2022 MPD Production

Second September 2022 MPD Production

November 2022 MPD Production

December 2022 MPD Production

January 2023 MPD Production

January 2023 MPD Production  (regarding undercover accounts)

March 2023 MPD Production

First April 2023 MPD Production

Second April 2023 MPD Production

First May 2023 MPD Production

Second May 2023 MPD Production

Third May 2023 MPD Production

Fourth May 2023 MPD Production

First June 2023 MPD Production

Second June 2023 MPD Production

Dataminr Emails

Reproduced Documents

Sent August 23, 2023

Sent August 24, 2023

Sent August 25, 2023

Sent September 5, 2023

Sent September 13, 2023

Sent September 20, 2023

Sent October 2, 2023

Sent November 28, 2023

2020: February , May , June , July , August , September , October , November , and December

2021: January , February , March , April , May , June , July , August , September , November , and December

2022: January , February , March , April , May , June , July , August , September , October , November , and December

2023: January

Note: There are some months between February 2020 and January 2023 for which we did not receive demonstration reports, as well as some months for which we only obtained a small number of demonstration reports. It is unclear whether the MPD assembled reports corresponding to those periods.

Records Show DC and Federal Law Enforcement Sharing Surveillance Info on Racial Justice Protests

Officers tracked social media posts about racial justice protests with no evidence of violence, threatening First Amendment rights.

Documents Reveal How DC Police Surveil Social Media Profiles and Protest Activity

A lawsuit forced the Metropolitan Police Department to reveal how it uses social media surveillance tools to track First Amendment–protected activity.

Study Reveals Inadequacy of Police Departments’ Social Media Surveillance Policies

Ftc must investigate meta and x for complicity with government surveillance, we’re suing the nypd to uncover its online surveillance practices, senate ai hearings highlight increased need for regulation, documents reveal widespread use of fake social media accounts by dhs, related resources.

Brennan Center Files Public Records Requests for Information on Social Media Monitoring

The Brennan Center for Justice filed public records requests in Boston, New York City, Los Angeles, Baltimore, and Washington, DC seeking information about their police departments’ use of social media monitoring.

Directory of Police Department Social Media Policies

Principles for social media use by law enforcement, informed citizens are democracy’s best defense.