online behavior essay

Can we better understand online behavior? These researchers will dig deep to find out.

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In the 21st century, our lives are online: Around the world, we can shop, socialize, bank, attend events, visit doctors, watch TV, listen to music, order takeout, work, learn, and much more, all with an internet connection. However, a vast majority of these activities are facilitated by a handful of digital gatekeepers, leaving everyone else with no meaningful way to parse how people are using those platforms or how those platforms are using their customers.

Researchers at Northeastern University were awarded a $15.7 million grant from the National Science Foundation to build a research infrastructure that will provide scientists around the world and across disciplines with open, ethical, analytic information about how people behave online.

“This would be a platform for research on basic human behavior,” says David Lazer , university distinguished professor of political science and computer sciences, and co-director of the NULab for Texts, Maps, and Networks , who is leading the project.

Christo Wilson , associate professor of computer sciences, and David Choffnes , associate professor of computer sciences and executive director of the Cybersecurity and Privacy Institute , are also working on the project, along with John Basl , associate professor of philosophy at Northeastern, and Michelle Meyer, a bioethicist at Geisinger Health System .

Up to this point, researchers who study any aspect of online behavior or the platforms that enable it have done so by using a few techniques: They’ve used bots to scrape data data from platforms, or used small samples of people who provide data about their online behavior, or sometimes requested or purchased specific sets of data from the platforms directly.

The strategies have drawbacks, however: Data is collected on a case-by-case basis, creating bespoke silos of information but not a wide picture of life online.

“Much of the research on digital traces is akin to searching by the lamppost in the parking lot for your keys, rather than in the dark where you dropped them,” the researchers write in their proposal.

Recruiting small samples of people to provide their own online data can also be expensive, thereby limiting the extent to which scientists can afford to study life online at all. Finally, online companies can be opaque in their methods for collecting and disseminating their data.

“People’s online lives are their lives; they’re inseparable,” Wilson says, “and these experiences are being shaped by platforms that aren’t transparent about how they make decisions. The fact that we can’t introspect what have really become pillars of modern life is a problem.”

The development of the research infrastructure is still in early stages, but the researchers proposed that it would work generally as follows:

• Northeastern scientists will recruit a small sample of people (roughly 2,000) for a rigorous examination of their online behavior, including how and how often they use major platforms. Then, they’ll recruit a larger sample (tens of thousands of people) to assess broader population trends, using the more granular information from the smaller sample as the gold standard to calibrate for the larger.
• The researchers will build a web browser extension to collect data about the URLs the volunteers visit and what they search for, from their laptops and desktops. To capture information from mobile devices, the researchers will build out apps for both Androids and iPhones that will enable limited collection of network traffic from the devices.
• In both cases—on a desktop or a mobile device—volunteers will also be prompted to fill out short surveys about their choices online, the researchers say.

The team will include two ethicists, Basl and Meyer, among its core researchers, and the research itself will follow strict regulatory compliance. The researchers, experts in cybersecurity and privacy issues, will also take every measure to ensure the data they collect is stored securely in private servers and available for scholarship in a format and with a process that protects the volunteers’ privacy.

Indeed Basl’s interest in the project, he says, is in studying existing data privacy norms followed by online platforms as a means for bolstering them. 

“One of the challenges we face in the project is that existing privacy norms, standards, and tools are often inadequate for promoting and protecting privacy in the face of big data analytics,” he says. “So, I’ll be watching and working to help ensure that we are cognizant of this issue, and work to develop new ways to evaluate, understand, and protect privacy.”

This infrastructure would provide myriad other opportunities for research, too.

Economists could study how money flows online; political scientists could study how people gather information ahead of elections; social scientists could study how social media platforms shape the information landscape in ways that have real-world consequences.

The computer science inquiries are vast: Researchers could study how algorithms influence people’s individual experiences online, or the extent to which individual data is being collected and used by corporate entities.  

“Nothing like this has ever been done before,” Choffnes says, and the possibilities for research based on the data he and his colleagues collect are nearly limitless. “It would shine a light on dark areas of the world, not just for computer scientists, but for anyone who studies anything on the internet.”

The researchers compare this infrastructure to the Hubble telescope: another powerful tool for scientific inquiry and exploration.

“Science is in part based on the tools of the scientists, and we don’t have adequate tools to answer our questions—yet,” Lazer says.  

For media inquiries , please contact Shannon Nargi at [email protected] or 617-373-5718.

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The Social Net: Understanding our online behavior (2nd edn)

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In 2005, The Social Net was a pioneering project to bring together contributions from leading scholars on the major topics pertaining to the social aspects of the online world. The book has been a great success and has helped many, including students, academics, and lay people, to attain a broad comprehensive knowledge of online social psychology. One of the leading aims of the book was to demonstrate the significant role the Internet plays in so many aspects of people’s social lives. Judging from the feedback it received, it appears that the book successfully fulfilled this purpose, and some people even suggested that ahead of its time, this volume predicted the colossal impact of the social networks. Now 7 years on, with around two billion people online, and countless websites covering all aspects of our existence, it is time to update this knowledge. During these years a great many articles have been published in this field, and it is important to bring this knowledge together into a more coherent whole. The book has been extended significantly to cover this rapidly growing phenomenon of human behavior in cyberspace. An A-team of leading scholars have produced an exciting new edition of The Social Net which promises to be on the cutting-edge of this dynamic field. Topics include: The Internet and personality, social cognition, identity manipulation, online romantic relationships, social influence on the Internet, online decision-making, the Internet and aggression, prosocial behavior online, online group processes, e-leadership, online prejudice and discrimination, online Intergroup contact, and online research. The book provides a comprehensive picture of the main areas of social psychology in the Internet arena and shows clearly that an understanding of the net cannot be limited to the technological aspects, for without an appreciation of the human factor involved, our grasp of this medium must be incomplete

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Essays on user behavior in online platforms

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Home — Essay Samples — Sociology — Mass Communication — The Netiquette and Its Significance in Online Behavior

The Netiquette and Its Significance in Online Behavior

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Words: 891 |

Published: Aug 14, 2023

Words: 891 | Pages: 2 | 5 min read

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The importance of netiquette.

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ORIGINAL RESEARCH article

Understanding online behavior: exploring the probability of online personality trait using supervised machine-learning approach.

• Information Assurance and Security Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

The notion of online anonymity is based on the assumption that on the Internet the means of identification are limited to network and system identifiers, which may not directly relate to the identity of the user. Personality traits as a form of identity have recently been explored. A myriad of relationships between the Internet and human personality traits have been examined based on correlation and regression of media usage specific to selected media platforms, such as social networking sites. In these studies, the link between humans and the Internet based on interests and disposition was studied. However, the paradigm of the existence of a platform-independent digital fingerprint of personality trait is yet to be explored. This paradigm considers the Internet an extension of human daily communication that is capable of exhibiting a digital behavioral signature. Therefore, in this study, using client–server interaction as the fundamental unit of online communication, the probability of a digital personality trait distinction was explored. A five-factor model of a personality trait measurement instrument and server-side network traffic data collected over 8 months from 43 respondents were analyzed using supervised machine-learning techniques. The results revealed a high probability that the signature of conscientiousness personality trait exists in online communication. This observation presents a novel platform for the exploration of online identity. Furthermore, it charts a new research focus on human digital signatures capable of characterizing online behavior.

Introduction

Human–computer interaction (HCI) is a convolutional discipline that integrates diverse research disciplines. Research on HCI involves the study of broader societal implications and interactions that are based on computer system usage ( Hooper and Dix, 2013 ). The integration of human psychological studies in HCI studies for understanding human dynamics on the Internet has received minimal ( Hooper and Dix, 2013 ) attention, with online behavioral identity being one major aspect. Research on human behavioral identity on the Internet integrates HCI and Web science such that traditional identification mechanisms (examples include network domain identifiers, security, and authentication tokens) are supplemented to provide a more reliable identification and profile-building process. Initial exploratory studies on the Internet revealed behavioral tendencies, such as loneliness compensation tendencies ( Amichai-Hamburger et al., 2002 , 2004 ; Ross et al., 2009 ), separation from family and depression ( Amichai-Hamburger, 2002 ), blogging and mass media usage patterns ( Guadagno et al., 2008 ; Schrammel et al., 2009 ; de Oliveira et al., 2011 ; Quercia and Kosinski, 2011 ; Moore and McElroy, 2012 ), and even addictive tendencies ( Samarein et al., 2013 ). The existence of these tendencies can be attributed to the nature of the Internet, which provides a suitable platform for the integration of domestic, professional, and family life and social desires, as well as for the exhibition of inherent desires. Such a platform presents a paradoxical agent capable of revealing the personality of online users. In other words, the Internet presents an integrated platform for the identification and simplification of the complex human identity.

Identity is an important factor in Web science and computer usage, which is conceptualized into domain identification ( Joiner et al., 2007 ), including physiological biometrics, social identity, technical identity, and behavioral biometrics. Human personality traits constitute the most common behavioral biometric adopted for the Internet user identification process ( Amiel and Sargent, 2004 ; Guadagno et al., 2008 ; Correa et al., 2010 ). Personality traits are variables that coordinate human action and experience through dynamic psychological organization and they constitute a major discriminant for determining online behavioral patterns ( Amichai-Hamburger, 2002 ). Trait theory is characterized by two fundamental tenets: quantification and cross-situational consistency. Personality traits as measured by the five-factor model (FFM) or the Big Three models ( Matthews et al., 2003 ) have been observed to satisfactorily meet these tenets. Additionally, it has been observed that they adequately capture human interaction on the Internet.

Research questions that involve the frequency of online media usage, the demographic composition of online users, the relationship between individual differences and online media usage and motives for online interaction, and the probable relationship between Internet users and their probable preference have been addressed ( Joiner et al., 2007 ; Guadagno et al., 2008 ; Correa et al., 2010 ; Davis and Yi, 2012 ; Moore and McElroy, 2012 ). These studies assessed the influence of various personality traits on Internet usage, based on the assumption that the Internet cannot replace human communication and entertainment. While such an assumption holds true in a larger aspect, one key component for understanding Internet and human interaction, that of an online behavioral pattern among individuals who share a similar personality, remains largely ignored. It has been asserted that the Internet is directly or indirectly related to individual personality traits ( Amichai-Hamburger et al., 2002 ; Amiel and Sargent, 2004 ; Ross et al., 2009 ) and is mostly under the control of the individual and is a moderating platform for expressing anonymized identity ( Young and Rodgers, 1998 ; Tan and Yang, 2012 ; Samarein et al., 2013 ), as well as a salient predictor of cyber space usage ( Guadagno et al., 2008 ; Golbeck et al., 2011 ; Davis and Yi, 2012 ). However, the question regarding the existence of personality trait signatures on the Internet remains unanswered.

In de Oliveira et al. (2011) , the probability that the personality trait of mobile phone users can be inferred based on trait mean score and call pattern was explored. Similarly, in Murray and Durrell (2000) , the probability that the demographic attributes of online users can be inferred was investigated. In Ross et al. (2009) , it was suggested that research studies targeting social networking sites, such as Facebook, Twitter, and LinkedIn, are angled toward the investigation of personality presentation on the Internet. The verification that a personality signature on the Internet exists involves observing the dichotomization of personality factors ( Ross et al., 2009 ; Amichai-Hamburger and Vinitzky, 2010 ; Moore and McElroy, 2012 ) to determine distinctive characteristics among various individuals on the trait continuum. This study attempts to answer a primal underlying question: “Can the personality traits of an individual be inferred from his/her network traffic?” This question aligns with the logic that human recurrent daily behavioral patterns are a subject of the inherent personality trait, which regulates the synergy between online and offline behavior. However, a reliable answer to this question requires a platform- or application-independent network data source. Existing studies in the literature are limited to the platform of interest, such as e-mail, blogs, or Facebook, which induces behavior peculiar to its features and application. Various assertions about personality trait based on platform-dependent Internet sources are presented in Table 1 .

Table 1. Summary of assertions on personality trait .

Personality Trait and the Internet

The use of the personality trait FFM in Web science research, which consists of openness to new experience, conscientiousness, extraversion, agreeableness, and neuroticism, allows for a common vocabulary and metrics for investigating and understanding individual dynamics. The study presented in Golbeck et al. (2011) showed that humans reveal their personality trait in online communication through self-description and online statistical updates on social networking sites through which the FFM can provide a well-rounded measure of the human–computer relation. The study observed that the personality trait of users can be estimated (in social media) to a degree of ≅11% accuracy for each factor based on the mean square error of observed online statistics. This implies that personality trait prediction can be achieved within 1/10 of its actual value. In Guadagno et al. (2008) , a similar inference based on blogging behavior was observed. The study explored the correlation between blogging and openness to new experiences as well as neuroticism. Similarly, a study reported in Lim et al. (2006) revealed that the temporal variability in e-mail delay and response can be adopted to infer the personality trait of the individual involved. In Salleh et al. (2010a , b , 2014 ), it was observed that the personality trait of an individual can be inferred from his/her pair-programing tendency.

The growing tendency to use individual personality traits in online studies indicates that personality traits constitute an online biometric modality for understanding and identifying online users ( Delgado-Gómez et al., 2010 ). This paradigm is widely applied toward the comprehension of individual personality and online social media. Social media in this context refers to online platforms where an individual’s consumption of digital media is channeled for interaction and/or expansion of social influence through online media, intention notwithstanding. Research on the relationship between an individual’s personality and media consumption has explored correlation and regression, as shown in Table 1 . In Correa et al. (2013) , it was asserted that the synthesis of individual psychological make-up presents the capacity to reveal Internet usage. This assertion was further supported in Amichai-Hamburger (2005) , where Internet usage was claimed to be dependent on the personality trait of the individual. The study suggested that the influence of personality traits can be observed in the duration of the individual’s online browsing period and tendency to use the Internet. The duration of the online browsing period reflects the individual’s choice, preference, and reflexes in cyberspace, which is largely controlled by his/her unique and stable psychological characteristics ( Correa et al., 2013 ). Therefore, the browsing duration can reflect the tendency of loneliness, since highly neurotic individuals and introverts tend to spend more time on the Internet to compensate for a probable lack of physical interaction, while at the same time, it projects the interest of “real self” exploration in online interaction ( Amichai-Hamburger, 2005 ; Schrammel et al., 2009 ). The tendency to use the Internet is defined in the context of the “rich get richer” and the “poor getting rich” theory ( Amichai-Hamburger, 2002 ). For example, in Amichai-Hamburger and Vinitzky (2010) , it was observed that individual high on the extraversion scale tend to use social media to enlarge their boundary of friends and influence, while individuals scoring high on the neuroticism scale tend to use anonymized online media for personal expression. In Hamburger and Ben-Artzi (2000) , it was further suggested that the Internet can be described as a complex platform that presents a diverse paradoxical lexicon. The study, however, highlighted that Internet usage in itself does not explain the causation of individual usage (dis)similarity. As highlighted in Table 1 , there appears to be a general consensus on the positive relationship between neuroticism and Internet usage, in particular on an anonymized channel.

Conversely, contrasting assertions seem to have been made about the relationship between personality trait factors and Internet service usage. For instance, in the study reported in Ross et al. (2009) , which was grounded in a self-report measurement instrument, it was observed that conscientiousness is not a predictor of social networking. However, in the study reported in Amichai-Hamburger and Vinitzky (2010) and Moore and McElroy (2012) , it was observed that conscientiousness is a predictor of online social networking. It is important to note that in Moore and McElroy (2012) and Amichai-Hamburger and Vinitzky (2010) the individual’s profile was adopted as the measurement instrument, while in Ross et al. (2009) , a self-report measurement instrument was used. Intuitively, the degree of observed correlation is dependent on the reliability of the measurement instrument. The features observed in the measurement instrument form the basis for the effective assertion that is predicated on the tempo-spatial properties of the data. Table 2 presents a synopsis of the attributes considered in the studies in the literature on personality and the Internet. However, the features considered in these studies were platform- or application dependent. The observation reported in Schrammel et al. (2009) and Moore and McElroy (2012) substantiates the importance of the reliability of the data-centric measurement instrument. In order to study online patterns, tempo-spatial features that are independent of platform or application are required. In studies on online user identification ( Herder, 2005 ; Padmanabhan and Yang, 2007 ; Kumar and Tomkins, 2010 ; Yang and Padmanabhan, 2010 ; Abramson, 2012 ; Herrmann et al., 2012 ; Abramson and Aha, 2013 ; Abramson and Gore, 2013 ), such platform-independent features, which include but are not limited to Web page visit characteristics, Web request characteristics, Web session characteristics, and Web genre characteristics, were adopted.

Table 2. Summary of features used in personality trait studies .

The integration of these applications and platform-independent features results in a robust mechanism for exploring individual behavior on the Internet. This study observed the probability of the existence of digital personality traits based on platform-independent features. It, thus, differs from existing studies as follows.

• The features considered are based solely on human action and are platform independent. Semantic structures in the observed features are adapted for pattern classification.

• The personality trait signature is considered based on the classification of trait dichotomy, in contrast to the correlation and regression of trait mean score. Dichotomy is defined in this context to mean a categorization of continuous variable as stipulated in Ören and Ghasem-Aghaee (2003) .

• The measure of experimental repeatability and validation is based on a standard perspective of measurement in addition to the generic method of dichotomization of traits ( Oren and Ghasem-Aghaee, 2003 ). This differs from the n-sigma thumb rule and equal thirds method applied in Ross et al. (2009) , Amichai-Hamburger and Vinitzky (2010) , and Moore and McElroy (2012) or the 40:30:30 dichotomy applied in Salleh et al. (2010a , b , 2014 ). The intuition behind the generic dichotomy is based on the limitation inherent in a data-centric dichotomy. A data-centric dichotomy yields varying borderlines for every dataset as revealed in the dichotomy observed in Ross et al. (2009) and Amichai-Hamburger and Vinitzky (2010) .

According to these observed distinctions, current study focuses on answering the question: given a dichotomous continuum of personality trait, do individuals in a dichotomy exhibit a consistent signature distinguishable from individuals in another dichotomy? To answer this research question, two key assumptions were considered:

1. An FFM of a self-report personality trait instrument is sufficient to describe an individual on the personality trait continuum.

2. The self-report instrument of an individual is independent of other individuals.

These assumptions provide a singular composition of the individual for which a dichotomization and subsequent classification process can be performed as detailed in subsequent sections.

In order to examine the probability that a personality trait distinction based on online interaction exists, server-side network data (from the fundamental building block of the Internet, client–server communication) were adopted. Additionally, the FFM personality trait measurement instrument was administered. Server-side network data was collected from the functioning servers in the Research Management Centre (RMC) in Universiti Teknologi, Malaysia for a period of 8 months. The network data inclusion in this study was conditioned on two criteria.

• The observed client (computer in this case) is used by only one individual throughout the duration of the data collection.

• Each client communicated frequently with the server for the duration of the data collection.

The RMC server is a research and development information system server that host research and daily operational activity for academic and non-academic staffs of the university. Network data collected from the server is in the form of log activity of each sampled user as discussed in the subsequent subsection.

Sample and Procedure

Server data were captured at the RMC server using a URL-request dump script that records the activity of each client in the organization. In order to enroll users for this study, proposal for the study was initially sent to the Director of the Research Centre where the Ethics committee recommended its approval. Furthermore, consent forms were distributed to RMC staff members. A total of 64 staff members volunteered for this study. Daily monitoring of the physical presence of these 64 staff members was conducted to satisfy the network data collection criteria. The 50-item International Personality Item Pool (IPIP) measure of personality trait was administered to the 64 respondents. However, only 43 respondents satisfied the network data collection criteria. Hence, this experimental study used 43 respondents, which accounts for 67% of the users who volunteered. An exploratory analysis of the 43 responses yielded a Cronbach’s alpha reliability, presented in the comparative analysis in Table 3 .

Table 3. Descriptive analysis of measured items .

The Cronbach’s alpha reliability of the conscientiousness personality trait (0.734) was observed to be closer to the reference IPIP-value (0.790). In addition, the conscientiousness personality trait have higher distribution of respondents as reflected by the value of its mean and SD (2.61 ± 1.02). Thus, for the analysis of network data, in this study, respondents were considered based on their conscientiousness trait (as highlighted in bold in Table 3 ). Conscientiousness personality trait is a continuous dimension of personality trait that describes individual tendency to demonstrate thorough and careful thought process, efficient and organized method of handling task, and systematic behavioral tendencies. The choice of conscientiousness personality trait does is primarily constrained to the reliability of the measured instrument as reflected in the Cronbach’s alpha. Coincidentally, the choice of conscientiousness personality trait will also provide a better alternative to the contrasting correlational studies between Ross et al. (2009) and Amichai-Hamburger and Vinitzky (2010) and Moore and McElroy (2012) . The network traffic of each respondent in the Conscientiousness personality trait was collected from 26th April 2014 to 31st December 2014.

Network Feature

A heuristic methodology was developed to clean the raw log file of the requested URL and to extract relevant human-centric features. The heuristics consider Web requests that originate as a result of human action, as opposed to requests initiated by a system or network facility on behalf of the individual. The heuristics were applied to individual requests and the following human-centric features were extracted based on a 30-min session boundary, which is the generally accepted session duration ( Kumar and Tomkins, 2010 ; Yang and Padmanabhan, 2010 ). Network features considered in this study is based on the human-centric characteristic features defined in Adeyemi et al. (2014) . The features that represent behavioral characteristics are intrinsic to human routine. Such behavior have been collectively applied in human studies ( Adeyemi et al., 2014 ). These features are elucidated in the following subsections.

Web Request Characteristics

The individual Web request pattern was observed through the inter-request characteristics extracted from each session. Inter-request time (also referred to as interval) is the time difference between two consecutive requests within a session. The statistical properties of Web request characteristics as defined in Adeyemi et al. (2014) , which include mean, SD, variance, kurtosis, and skewness of individual Web requests, were extracted from each session. These standardized features were considered with respect to interval and flight time. A total of 10 human-centric features were extracted from the Web request characteristics.

Visitation Pattern

The University Centre operates a two-server load-balancing client–server communication architecture. This implies that the possible number of probable Web pages is bounded by the total Web pages in the two servers as represented by

s = total number of servers and N = number of unique URLs in each server.

In this study, it was assumed that individual Web request patterns obey a power law distribution as asserted in Barabasi (2005) and Zhou et al. (2008) from empirical experimentation. The visit characteristics considered in this study include aggregation of visit within session, rate of revisit per session, and session length with respect to visit aggregation, presented in Eqs 2–4, respectively.

The logic of rate of visit is in conformity with Eq. 1, on the premise that the probable URLs that an individual can visit are limited to the observable URLs in the server. In addition, this presupposes that the interest-driven model and priority-queue model of probable request patterns ( Zhou et al., 2008 ) are captured by the bounded URL distribution such that all the observed users share similar working conditions and the major observable distinction can be revealed through observing the human behavioral composition. Three features were derived from the visitation pattern. In addition, session duration and total number of requests per session were also derived. A total of 15 features were extracted from the network traffic as summarized in Table 4 .

Table 4. Summary of features used in classification process .

The duration of the server-side data collection was divided into a pattern observation (training) phase and pattern validation phase. For the model training and validation phases, 21 and 15 weeks were adopted, respectively. In order to explore the distinction among the observed dichotomies, six supervised machine-learning algorithms were examined. The selection of the six classifiers was based on the initial exploration of applicable classifiers using the extracted features. Twenty two supervised classifiers were initially explored. This include Baseline classifier, BF tree, Decision stump, Hoeffding tree, J48, Logistic Model Tree (LMT), NB tree, Random forest, Random tree, Naïve Bayes, Bayes Net, Simple logistics, Hidden Markov Model, SMO, SVM, multilayer perceptron, Decision table, JRip, Partial Decision Tree (PART), k-NN, Decision Table Naïve Bayes (DTNB), and logistics regression model. Six classifiers performed significantly better than the baseline classifier. The six classifiers consisted of a logistic regression model, LMT, J48 decision tree, Reduced Error Pruning Decision Tree (REPTree), DTNB, and PART. Discussions of these classification algorithms can be found in Kotsiantis et al. (2007) , Othman et al. (2007) , and Nguyen and Armitage (2008) .

The process adopted in this study to explore classification is similar to that defined in Kotsiantis et al. (2007) , as presented in Figure 1 . However, the exploration process applied in this study included an exhaustive search method for finding an applicable classifier. This involves a search for all the applicable classifiers capable of establishing a discriminative boundary among classes in the dataset based on the informative structure of the feature space. The process starts with the arrangement and sorting of the data to obtain uniformity. The result of this process is then input into the pre-processing section. Pre-processing involves data cleaning, extraction of the sequence of request, and sessionization of the request based on the adopted session threshold. The next stage involves splitting the dataset into training and testing samples. This is followed by the classifier exploration process. This process involves selecting a classification algorithm, splitting the dataset into training and testing, and then comparing the accuracy of the algorithm with the baseline accuracy. The default baseline for the exploration process is based on the highest class probability that can be measured by the ZeroR algorithm in the WEKA ® tool kit. A classifier is considered applicable if the attained accuracy is significantly better than the baseline accuracy. Some classifiers, such as the logistic regression model, require parameter optimization through parameter tuning. For such a classifier, tuning is conducted to verify the applicability of the classifier to the feature space. A classifier that meets these criteria is then accepted. However, a classifier is considered inapplicable when is it not capable of establishing a discriminative boundary among the classes in the dataset as presented in the feature space, as depicted in Figure 1 .

Figure 1. Procedure for classifier exploration .

WEKA software was adopted for the classifier exploration process in this study. This is because it is a Java-based open source software that has gained widespread adoption for pattern classification and machine-learning processes because of its robustness to feature size, ease of integration ( Othman et al., 2007 ) and within-script automation ( Yang, 2010 ). The experimental process was based on the accuracy obtained using 10-fold cross validation and a 10-iteration process to prevent overfitting. The default setting in the WEKA tool kit was adopted for all parameters in the selected classifiers.

To evaluate the performance of each classifier, seven evaluation metrics were considered: accuracy, Kappa Statistics, root mean square error (RMSE), precision, recall, F-measure, and area under the receiver operating characteristics curve (AUC). The accuracy of each classifier is described by the degree of difference between the correctly classified [true-positive (TP) and true-negative] instance and the actual instance. The RMSE measures the magnified difference between the correctly classified instances and actual instances. RMSE (range from 0 → 1) is biased toward larger errors, a characteristic that makes it suitable for prediction performance evaluation. Precision (0 → 1) computes the ratio of correctness over the classified instances. It describes the consistency of the classifier. Recall (0 → 1) evaluates the performance based on the probability of the correctly classified instance. AUC (0 → 1) is the cumulative distribution function (CDF) of the TP to the CDF of the false-positive (FP). F-measure (0 → 1) measures the average rate of precision and recall of a classifier. It balances precision/recall trade-offs. Kappa (Cohen’s kappa coefficient) statistics, however, measures the accuracy with respect to the p -value; thus, Kappa statistics measures the coincidental concordance between the output of a classifier and the label generation process. It compensates for random accuracy in a multi-class phenomenon. Its values range from −1 (total disagreement) through 0 (random agreement) to 1 (complete agreement), which implies that the computed accuracy depends on the efficiency and effectiveness of the classifier for the given observation.

Measurement Item Dichotomy

In order to define class membership based on the FFM domain score of each respondent, the dichotomy defined in Figure 2 was adopted. High-, moderate-, and low-class dichotomy was obtained for 21, 12, and 10 respondents, respectively. A statistical t -test revealed a statistically significant difference between the mean of the observed dichotomies, which suggests a high inter-class boundary and low intra-class boundary. In order to prevent data redundancy and enhance computational efficiency, only individuals who exhibited unique request patterns were considered for inclusion in the pattern observation process. A unique request pattern was observed based on the discretization and symbolic translation of individual inter-request patterns. It was observed that the overall session instance and size of respondents for each dichotomy were reduced by 27.3, 8.33, and 5% for the low, moderate, and high classes, respectively, for the training dataset.

Figure 2. Adapted cut-off for trait score dichotomy/class formation .

In order to explore the probability that a digital personality exists on the Internet, the conscientiousness trait was dichotomized to form clusters of the low, moderate, and high classes as depicted in Figure 2 based on the assertion in Oren and Ghasem-Aghaee (2003) that identified a five-class dichotomy on the personality trait continuum. The adopted cut-off is independent of the distribution of the data. This is to prevent a data-centric dichotomy, while only three dichotomies were extracted from the trait score distribution. Six machine-learning classification schemes were applied to the extracted features to observe the structural relationship capable of revealing a dissimilar inter-dichotomy pattern. The schemes included J48 decision tree, a logistic regression model, LMT, DTNB, REPTree, and PART. Decision trees (DTs) are capable of presenting a high-level abstractive relationship between observable variables and lend themselves to ease of computation. Initial observations revealed that the observed schemes exhibit a higher classification accuracy than other types of classification scheme.

Signature Observation Based on Training Data

In order to examine the probability of distinguishing individuals based on the conscientiousness dichotomy and consequently to answer the research question, eight standardized machine-learning evaluation criteria were considered. The results of the experimental process are presented in Table 5 . The experimental process was based on 10-runs of 10-fold cross validation. The selection of the observed classifier was based on a preliminary investigation to find a suitable classifier and on the preference that the results obtained should be logically interpretable. In addition, tree-based classifiers have been widely applied in online user identification ( Yang and Padmanabhan, 2010 ). The results revealed that five classifiers, DTNB, PART, J48, LMT, and REPTree, achieved statistically significant classification accuracy relative to the baseline accuracy. The baseline classifier achieved on average an accuracy of 48.81%. However, DTNB, PART, J48, LMT, and REPTree achieved an average accuracy of 79.21, 76.66, 82.36, 84.96, and 80.74%, respectively. The LMT classifier attained a higher accuracy in distinguishing individuals along the conscientiousness personality trait continuum with a noteworthy low error rate (Type-I and Type-II), which indicates that the achieved results are reliable. The research question posed in Section “Personality Trait and the Internet” of this paper is answered using a statistical significance level of p > 0.001. The obtained accuracy for each classifier was measured relative to ZeroR, the baseline classifier adopted in this study. Table 5 shows a baseline accuracy (ZeroR classifier) of 48.81%, which forms the null hypothesis (the probability of obtaining an accuracy level lower or equal to the baseline could be explained by random variation) for the study. The accuracy achieved by the logistic regression model is statistically insignificant. This implies that the obtained accuracy using the logistic regression model can be explained by random variation. However, the other classifiers, DTNB, PART, J48, LMT, and REPTree, achieved a statistically significant accuracy level. The statistical significance of the results of these classifiers implies that the achieved accuracy is a function of the structural composition and efficiency of the classifiers.

Table 5. Result of signature pattern exploration .

The performance of LMT is significantly superior (as highlighted in bold in Tables 5 and 6 ) to that of the baseline classifier based on the value of the AUC (plot of the false-positive rate versus the true-positive rate). The AUC value ranges from 0 to 1, where a value <0.5 indicates that the result of the classifier is not better than a random guess. Values closer to 1 indicate that the classifier is robust and reliably accurate. AUC is robust to imbalanced data, thus providing a reliable metric that indicates how well a classifier separates the classes in the dataset. The AUC value (averaged at 0.94) shows that the LMT classifier can correctly separate instances (conscientiousness trait) of respondents into their appropriate dichotomies. Similarly, the F-measure, averaged at 0.89, indicates that LMT provides a reliable discriminatory boundary detector for the various classes in the dataset. The F-measure is the harmonic mean of precision and recall that indicates the precision recall property of a classifier. Furthermore, an assessment of LMT using RMSE and Kappa statistics indicated a fairly consistent performance. The RMSE value, averaged at 0.28, indicates that the LMT classifier can reliably estimate the posterior probabilities of each class. The RMSE value ranges from 0 to 1, where a value closer to 0 reflects the capability of the classifier to reliably estimate the posterior probability of each class in the experimental data.

Signature Consistency Based on Validation Dataset

In order to ascertain and verify the observed reliability of the results presented in Table 5 , a separate validation dataset was evaluated. The results, shown in Table 6 , were based on the same experimental condition as the training dataset, revealing consistencies in the probability of distinguishing individuals on the conscientiousness personality trait continuum. The performance of J48 and LMT is consistently higher than that of the other classifiers. These results indicate a very high probability of the existence of a digital fingerprint along the conscientiousness continuum.

Table 6. Result of signature pattern validation .

The results in Table 6 further substantiate the existence of a digital fingerprint as asserted by the results shown in Table 5 . LMT performed consistently higher than the other classifiers. The average accuracy in the exploration phase and the validation phase shows a relative similarity at a value >80%, which suggests a statistically significant probability of the existence of a personality print. The results achieved by the five classifiers, as indicated in Table 6 , show a very high statistically significant classification of the data space of the validation dataset relative to the baseline accuracy classifier (ZeroR). The results presented in Table 6 are based on a class prior probability of 35.6, 39.3, and 25.1% for high-, moderate-, and low-class dichotomy, respectively. The observed average accuracy of LMT at 80.50% for the conscientiousness dichotomy, as demonstrated in Figure 3 , indicates that individuals can be distinguished on the conscientiousness continuum to accuracy strength of 7.2, 7.0, and 6.9 out of every 10 instances for the high, moderate, and low classes, respectively. Figure 3 further presents a comparative analysis of the accuracy for each class in the conscientiousness dichotomy. The parameters considered include the class prior probability of each class, the achieved accuracy of each class, the difference between the class prior probability, and the achieved accuracy. The results also indicate reliable internal consistency with the F-measure and AUC value at approximately 0.82 and 0.92, respectively. Furthermore, the sensitivity (recall) shows individual identification is reliable at approximately 0.82. This implies that for every given instance, the LMT model can correctly distinguish individuals on the conscientiousness continuum with 82% reliability. In order to verify the performance of LMT over the other classifiers (using paired t -test), the experiment was repeated in reference to LMT as the baseline classifier. The result of the test is presented in Table 7 .

Figure 3. Analysis of validation result .

Table 7. Analysis of significance test .

The statistical significance test was measured based on 10 repetitions of 10-fold cross validation result of each classifier (which generated 100 instances of accuracy for each classifier). Each classifier was paired with LMT and the sample mean of the pairs were tested on the assumption that there are no statistically significant difference between the achieved classification accuracy of LMT and each paired classifier. The result showed that the accuracy of LMT was statistically significant at 95 and 99% confidence interval, in reference to the other classifiers. This test, thus, lends further credence to the performance of LMT on the training and validation datasets.

Initial observation of the results of the validation process suggests higher accuracy for classification of the low class. However, a granular dissection of the accuracy strength reveals that the accuracy of the high class is slightly better than that of the other classes.

The research question of this study stated in the form of a hypothesis asserts that individuals can be distinguished in online interaction based on their position on the personality trait continuum. The results presented in Tables 5 and 6 support this assertion, showing a high probability of the existence of a personality trait signature. The dichotomy explored in this study is different from that adopted in Ross et al. (2009) , Amichai-Hamburger and Vinitzky (2010) , and Moore and McElroy (2012) . It also differs from the dichotomy considered in Salleh et al. (2010a) and Salleh et al. (2014) as well as from n-sigma rules. The defined dichotomy adopted in this study is considered generic and unbiased toward data skewness. Such generic scaling provides a basis for experimental repeatability and a corresponding comparison with subsequent studies. Earlier studies in the literature, defined dichotomies based on trait mean score distribution (equal thirds, for instance). Such a cut-off boundary is biased toward the distribution of the data, which amounts to different boundary cut-offs for every dataset [the dichotomies observed in Ross et al. (2009) and Amichai-Hamburger and Vinitzky (2010) , for example] and, hence, context dependent. Individuals classified as high class in one context can be classified as moderate in another. To frame research findings on such a basis contradicts the universality of the personality trait measurement instrument. Furthermore, an initial experimental evaluation based on the five-sigma rule and equal third dichotomy produced an accuracy below 10% for the prior probability of each class. The F-measure and sensitivity of each classifier based on the five-sigma rule were below 0.60. This suggests that a context-dependent dichotomy [such as presented in Ross et al. (2009) ] cannot provide a reliable measure for the exploration of the digital conscientiousness trait. The results shown in Tables 5 and 6 consistently show the capability of the explored dichotomy.

The results further demonstrate the superiority of the LMT classifier to other classifiers in terms of overall accuracy. An LMT classifier is a hybrid classifier that integrates a linear logistic regression model in a DT classification mechanism. Classification is achieved by generating decisions with logistic models at its leaves and the prediction estimate is obtained by using posterior class probability. The integration of DT into LMT enhances its superiority to linear regression models when applied to a highly multidimensional dataset that requires ease of human interpretability. The performance of the DTNB classifier was inferior to that of LMT in this study. DTNB constitutes the integration of a naïve Bayes algorithm in a decision table mechanism. An initial experiment based on naïve Bayes showed a very poor classification performance. The naïve Bayes classifier assumes that all the attributes in the dataset are independent. The capability of LMT to infer larger structural knowledge from a high dimension dataset can be attributed to its superiority to DTNB. PART is a rule-based induction algorithm that builds a DT by avoiding global optimization in order to reduce time and processing complexities. PART uses the separate-and-conquer approach of RIPPER and combines it with the DT mechanism of C4.5 by removing all instances from the training dataset that are covered by this rule and proceeds recursively until no instance in the dataset remains. PART builds a partial DT for the current set of instances by choosing leafs with the largest coverage as the new rule. However, LMT demonstrated a higher classification capability than PART in this study.

A REPTree applies regression tree logic and generates multiple trees in altered iterations by sorting the values of numeric attributes once. This is achieved through the information gain principle (which measures the expected reduction in entropy), tree pruning based on reduced-error pruning with a back fitting method, and integration of the C4.5 mechanism for missing values by splitting each corresponding instance into fractional instances. However, LMT demonstrated higher classification accuracy than REPTree in both the training and testing process. The J48 DT classifier is a Java coded version of the C4.5 DT implemented in the WEKA workbench. C4.5 is an induction-based learning algorithm that uses the information gain ratio, as opposed to the ordinary information gain, which is biased toward large value attributes, as the splitting criterion for recursively partitioning instances of attributes into attribute space. Instances are classified by constructing nodes that form the root of the tree using singular incoming edges to link nodes, while supporting multiple outgoing edges through a predefined discrete function of the input attribute value. The performance of LMT showed a higher classification accuracy than did that of J48 in terms of the measured parameters in Table 7 . This is further shown in the average number of correctly and incorrectly classified instances in Table 8 . A granular observation of the resultant model reveals that LMT generated a smaller size of rules for classification than did J48. However, the time taken to build the LMT model is significantly longer than that taken to build the J48 DT model. A comparative analysis of the performance of each classifier is presented in Table 8 . Parameters considered include the time taken to build each model (time elapsed), average number of correctly classified instances in 10 iterations (average number of correct), average number of incorrectly classified instances in 10 iterations (average number of incorrect), and number of rules or trees generated by the algorithm to learn the instances of an attribute for prediction (number of trees/rules).

Table 8. Comparative analysis of performance of classifiers .

As shown in Table 8 , the number of rules generated for classification is not a direct indicator of the effectiveness of the classifier. This is evident in the number of rules generated by LMT (903), J48 (1469), REPTree (779), and PART (181) in decreasing order of performance. In terms of time taken to build the model, REPtree outperformed all the other classifiers with an average number of 9019.3 correctly classified instances. Next in the performance rank based on the time taken to build the model is the J48 DT. The performance of this classifier is of particular interesting because in terms of its average number of correctly classified instances it is closer to that of LMT. Therefore, if time is considered a factor in the selection of a classifier, the J48 DT presents a better classification performance than LMT. Network forensic analysis processes (such as catch-it-while-you-can), which consider time an important factor, are an example of applications that favor the time taken to build the model. However, in a data analysis process, where accuracy is a critical factor irrespective of the time taken, the LMT classifier presents a better performance. Furthermore, the time elapsed (also referred to as the running time of a classifier) is dependent on factors, such as

i. the processor core; single versus multiple core system configuration,

ii. the generation of the processor core with respect to the read/write speed of memory,

iii. architecture: 32- or 64-bit, and

iv. input data to the classification algorithm.

The data input to a classifier determines its intrinsic classification complexity. WEKA measures the complexity gain of a classifier based on a log-loss function of base 2 of entropy gain. The complexity improvement column in Table 8 shows the level of complexity gain on the class prior entropy for each observed classifier. The LMT and DTNB classifiers demonstrated high complexity gain as compared to other classifiers. This further suggests the effectiveness of LMT in terms of the accuracy, overall complexity, and reliability of the developed model.

An example of such an application is the stop-look-and-listen type of network forensic process. A depiction of the DT generated by the J48 classifier is presented in Figure 4 . The DT from J48 is presented as against the DT from LMT model. This is because, LMT model generate rules that combines logistic model and decision tree that are relatively complex to interpret, as against rules generated from J48, and other rule-based classifiers. The figure illustrates a body of rules generated using the DT process of the J48 classifier. The results for the training dataset, as depicted in the partial DT presented in Figure 4 , show that individuals in each dichotomy exhibit structural patterns that integrate the following network feature combinations on the conscientiousness scale.

Figure 4. Partial decision tree of personality print .

In Figure 4 , it can be seen that when the rate of visit was ≤0.106557, the rate of visit-count per session was ≤10.8684, the rate of visit was <0.009975 and >0.00, the session duration was >320 s, the rate of visit-count per session was >7.655462, the session duration was >1227 s, and the rate of visit per session was ≤8.488285, a total of 142 structural patterns were extracted and all the patterns were observed to belong to individuals who scored low on the conscientiousness trait continuum.

Moderate Class

In Figure 4 , it can be seen that when the rate of visit was ≤0.106557, the rate of visit-count per session was ≤10.8684, the rate of visit was ≤0.009975, and the rate of visit was <0.00, a total of 47 structural patterns were extracted and all the patterns belonged to individuals in the moderate class of the conscientiousness trait continuum.

Top-down navigation of Figure 4 , right navigation to node N185, reveals that when node N185 was >4.352697 and the session duration was >1615, 100 structural patterns were extracted. Ninety-six of these were observed to belong to individuals in the high class on the conscientiousness continuum. Only four patterns belong to a different class. A further navigation through the DT space reveals that 18 structural patterns were also extracted and 17 of the 18 structural patterns belong to individuals in the high class of the conscientiousness continuum. The structures reveal that individuals in this class share a similar session duration, which is relatively higher than individuals in other classes.

These rules demonstrate the manner in which DTs can be used to classify an individual on the Internet into the low, moderate, or high classes on the conscientiousness continuum. The rules further show the relationship between the personality trait of online users and the observed usage pattern of the Internet. In essence, it shows that online users who are on the Low-class, Moderate-class, or High-class on the Conscientiousness personality trait continuum can be distinguished on the Internet within a 30-min session span. Since this study could not extract classes for very low and very high dichotomies, it is logical to assume that the novel signatures of individuals who belong to such dichotomies on the conscientiousness trait continuum are not captured in the present study. However, this result is particularly insightful because it uses a DT. A DT offers several advantages over other types of classifiers. These include ease of interpretation by humans and ease of presentation and application. Furthermore, a DT does not require a prior assumption about the structure of the data, but builds knowledge based on the structure of the data.

Implications

Given the advances in modern day technology, the Internet continues to present challenging requirements for effective service delivery systems, as well as reliable information security and assurance. The idea of the existence of a personality print on the Internet presents a paradigm that is capable of explicating and subsequently understanding the complexities of Internet technology. The notion of a personality print stands up to the challenge and ultimately debunks the general dictum of “on the Internet, no one knows you are a dog.” Personality print from a service delivery perspective such as e-commerce and e-learning, as well as recommender systems, presents a platform for the development of an intelligent Internet service system that is capable of detecting the characteristics of an individual and consequently predicting or suggesting effective personalized-service processes. An appropriate dissection of the personality identity of the end-user opens for researchers a more fundamental discourse on the underlying causation of network burstiness and the probability that sophisticated artificial networks capable of replicating or mimicking human dynamics can be built. Furthermore, it opens the path for researchers to better comprehend the evolution of Internet consumption. In terms of online security, the personality print introduces a complementary platform for online identification, specifically in one-to-many authentication processes. The integration of the personality print in the identification and authentication process further suggests a robust and reliable security mechanism laced with inherent human characteristics. The observed high probability of the existence of personality prints substantiates the assertions in studies in the literature related to personality and the Internet ( Ross et al., 2009 ; Correa et al., 2010 ; Moore and McElroy, 2012 ), such that a generic description of the relationship between an online user and an observed behavioral pattern of network features can be explained.

The observation in this study is limited to several factors. First, only one personality factor was observed in this study. This is attributed to the limited sample size of respondents in the study. The observed classification accuracy of the LMT is below 100%. This implies that the results obtained do not represent a perfect classification but do provide a probabilistic analysis of the existence of a personality print. Furthermore, only three classes out of five dichotomous distinctions defined in Figure 2 were explored in this study. This can also be attributed to the sample size adopted in this study. The integration of client-side network data into the pattern observation process presents a more comprehensive dataset for personality print exploration. An on-going process is being performed to expand this preliminary investigation, such that a larger sample size can be studied. In addition, other factors of the Big Five personality trait models are being explored.

Author Contributions

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

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This research was supported/partially supported by Universiti Teknologi Malaysia and Ministry of Higher Education Malaysia under the vote number: R.J130000.7813.4F804.

Supplementary Material

The Supplementary Material for this article can be found online at http://journal.frontiersin.org/article/10.3389/fict.2016.00008

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Keywords: personality print, personality trait, digital behavioral signature, human–computer interaction, logistic model tree

Citation: Adeyemi IR, Abd Razak S and Salleh M (2016) Understanding Online Behavior: Exploring the Probability of Online Personality Trait Using Supervised Machine-Learning Approach. Front. ICT 3:8. doi: 10.3389/fict.2016.00008

Received: 01 June 2015; Accepted: 21 April 2016; Published: 31 May 2016

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Copyright: © 2016 Adeyemi, Abd Razak and Salleh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ikuesan Richard Adeyemi, raikuesan2@live.utm.my

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Problematic Online Behaviors among Adolescents and Emerging Adults: Associations between Cyberbullying Perpetration, Problematic Social Media Use, and Psychosocial Factors

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• Kagan Kırcaburun 1 ,
• Constantinos M. Kokkinos 2 ,
• Zsolt Demetrovics 3 ,
• Orsolya Király 3 ,
• Mark D. Griffiths 4 &
• Tuğba Seda Çolak 1  

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Over the past two decades, young people’s engagement in online activities has grown markedly. The aim of the present study was to examine the relationship between two specific online behaviors (i.e., cyberbullying perpetration, problematic social media use) and their relationships with social connectedness, belongingness, depression, and self-esteem among high school and university students. Data were collected from two different study groups via two questionnaires that included the Cyberbullying Offending Scale, Social Media Use Questionnaire, Social Connectedness Scale, General Belongingness Scale, Short Depression-Happiness Scale, and Single Item Self-Esteem Scale. Study 1 comprised 804 high school students (48% female; mean age 16.20 years). Study 2 comprised 760 university students (60% female; mean age 21.48 years). Results indicated that problematic social media use and cyberbullying perpetration (which was stronger among high school students) were directly associated with each other. Belongingness (directly) and social connectedness (indirectly) were both associated with cyberbullying perpetration and problematic social media use. Path analysis demonstrated that while age was a significant direct predictor of problematic social media use and cyberbullying perpetration among university students, it was not significant among high school students. In both samples, depression was a direct predictor of problematic social media use and an indirect predictor of cyberbullying perpetration. However, majority of these associations were relatively weak. The present study significantly adds to the emerging body of literature concerning the associations between problematic social media use and cyberbullying perpetration.

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Recent developments have made the relationship between individuals, technology, and technological devices much closer. Although the Internet does not have a long history, it has become indispensable in contemporary day-to-day life. Nowadays, social media platforms are the most popular and widely used applications used on the Internet (Kemp 2017 ). According to the Internet usage results report of Wearesocial and Hootsuite digital marketing agency published in January 2017, the number of active social media users has increased by 21% all over the world compared with the previous year (Kemp 2017 ). Although social media is used for positive purposes such as meeting new people and socializing, maintaining existing relationships, and informational and educational purposes (Horzum 2016 ), some individuals (including high-risk groups such as students) demonstrate problematic online behaviors that may negatively affect them (Kuss et al. 2014 ). Cyberbullying perpetration (CBP) and problematic social media use (PSMU) are considered two such potential risky behaviors (Ayas et al. 2016 ). Using the problem behavior theory (PBT), the present study examines the relationship between PSMU and CBP and their relationships to social connectedness, general belongingness, depression, and self-esteem among high school and university students.

Problematic Social Media Use

Social media applications have become increasingly popular on the Internet. According to the Household Information Technologies Usage Rates reported by the Turkish Statistical Institute (Turkstat 2016 ), social media use is the most popular activity engaged in by individuals online (82.4%). Furthermore, a minority of social media users appears to be problematic users of these platforms and some individuals appear not to be able to control themselves (Kuss and Griffiths 2017 ). Despite the inconsistency on the definition of problematic social media use (PSMU) (Bányai et al. 2017 ), based on the biopsychosocial theoretical model, PSMU comprises mood changes and total preoccupation of using social media, having negative feelings and psychological symptoms when social media is unavailable, and having negative consequences in real-life areas because of excessive social media use (Bányai et al. 2017 ).

Cyberbullying Perpetration

Another problem behavior that is related to Internet technology is cyberbullying perpetration (CBP). CBP is defined as the use of Wi-Fi-enabled devices such as computers, tablets, and mobile phones by a person or group of people with the purpose of intentionally and repetitively behaving in a hostile way to harm the others (Hinduja and Patchin 2014 ). Losing control while using the Internet increases the probability of risky online behaviors such as CBP (Gámez-Guadix et al. 2016 ). CBP, which is reported as being frequent during adolescence (Yen et al. 2014 ), is a serious problem that affects 20–40% of young people (Tokunaga 2010 ). CBP appears to continue in adulthood but starts to decline during late adolescence (Antoniadou and Kokkinos 2015 ). The fact that cyberbullying perpetrators do not need to know their victim and do not see the results of their actions are among the factors that can increase the frequency of CBP (Campbell et al. 2013 ). Digital platforms where individuals are most exposed to or demonstrate CBP are via instant messaging and social media (Whittaker and Kowalski 2015 ).

Problem Behavior Theory

Jessor ( 1987 ) reported that problematic behaviors among adolescents such as committing crime and substance abuse originate from permanent characteristics of the individual, rather than an instant characteristic. In order to understand such problematic behaviors, research should focus on the interrelated factors of personality, perceived environment, and behavioral systems (Jessor 1991 ). A number of years later, Boyd et al. ( 2009 ) extended the conceptual model of PBT and asserted problematic behaviors should be understood in terms of an individual’s social and perceived environment, demographic characteristics, genetic/biological factors, and psychological characteristics including personality factors.

The problem behavior theory (PBT) can also be applied to problematic internet use. Many scholars have intuitively referred to the features of PBT in their accounts of pathological internet use without reference to the PBT specifically (e.g., Griffiths [2005] biopsychosocial model of addiction that also includes structural characteristics of the activity and the situational characteristics of the surrounding environment in which the behavior takes place). Over the past two decades, many scholars have adapted various incarnations of the DSM criteria for the pathological gambling and/or psychoactive substance abuse (see Kuss et al. 2014 for a review). When clinically assessed, addictions often represent the attempt of an individual to control depression and anxiety, while reflecting deep insecurities and the feeling of inner emptiness (Kim et al. 2006 ). In this context, based on the definitions of PBT, PSMU and CBP described above, PSMU and CBP can be considered as bearing the characteristics of potentially problematic online behaviors.

Despite the fact that the association between CBP and PSMU has yet to be empirically examined, previous research has reported an association between problematic Internet use and CBP among students (Casas et al. 2013 ; Eksi 2012 ; Kırcaburun and Baştug 2016 ). Consequently, the present authors hypothesize that CBP and PSMU are positively related. Furthermore, spending excessive time in platforms where problematic social interactions may occur is also likely to increase the probability of CBP. Kopecký ( 2014 ) suggested that social media platforms are where cyberbullying incidents are likely to occur.

Previous studies have also indicated that when problematic online behaviors are considered, gender is an important control variable. Female and male students have different usage aims, habits, and behaviors in online contexts (Kuss and Griffiths 2017 ). Furthermore, several studies have shown that female students are more prone to depressive moods (Nolen-Hoeksema and Girgus 1994 ), and to develop problematic use of online platforms containing higher social interactions (Andreassen et al. 2017 ). Based on recent previous studies (Andreassen et al. 2017 ; Kırcaburun and Tosuntaş 2017 ), it is hypothesized that females will demonstrate less CBP and more PSMU and depression. Age is another important control variable concerning CBP and PSMU. Several studies have shown that younger emerging adults and older adolescent students are more prone to depressive moods (Bayram and Bilgel 2008 ; Hankin et al. 2015 ), and therefore they may experience problematic online behaviors in to cope with the maladaptive depression. Furthermore, it has been suggested that depressive feelings and problematic behaviors may peak around the last years of high school (Bayram and Bilgel 2008 ; Hankin et al. 2015 ). Based on previous studies (Andreassen et al. 2017 ; Ševčíková and Šmahel 2009 ), it is hypothesized that younger emerging adults and older adolescent students will be more problematic social media users and cyberbullying perpetrators.

Self-Esteem and Depression

Individuals’ PSMU, which is arguably a “pure” form of Internet addiction or sub-type of Internet addiction because there is no offline equivalent (Kuss and Griffiths 2011 , 2017 ), have similar symptoms to the deprivation symptoms of substance abuse. For instance, in a study carried out by Woods and Scott ( 2016 ) with Scottish adolescents, it was found that the teenagers emotionally invested too much in social media during night and day, had low sleep quality, low self-esteem, high anxiety, and high depression levels. This may mean that if adolescents are not connected to social media, they may feel isolated and stressed, and may lead to increased anxiety and depression (Woods and Scott 2016 ). These symptoms can arguably be considered as indicators of deprivation. Various studies have shown that those operationally defined as addicted to Internet activities have low levels of self-esteem (Chang et al. 2015 ; Perrella and Caviglia 2017 ), and that positive or negative feedback to their online posts can affect their levels of self-esteem (Valkenburg et al. 2006 ). Individuals use social media platforms for many reasons, including (among others) seeking social support, appeasing their emotions, and coping with depressed psychological status. However, sometimes social media platforms have the opposite effects than to what is desired. For example, depressed individuals with low self-esteem share posts that reflect their negative psychological status to receive support from others, and if this behavior does not result in receiving the desirable reactions they may feel worse (Forest and Wood 2012 ). The use of social media as a means to increase self-esteem may, on the contrary, cause their self-esteem to decrease due to CBP (Patchin and Hinduja 2010 ; Radovic et al. 2015 ).

In sum, depression—which is strongly related to self-esteem (Orth et al. 2008 )—is an important construct that affects life satisfaction and psychological wellbeing, as well as influencing online behaviors among adolescents and young adults. Previous studies have demonstrated that PSMU and CBP are positively related to depression (Campbell et al. 2013 ; Chang et al. 2015 ; Fan et al. 2016 ; Jung et al. 2014 ; Kırcaburun 2016 ). Based on these studies, it is hypothesized that depression will positively predict CBP and PSMU. According to PBT, low self-esteem is among the personality risk factors that may lead to development of problem behaviors (Jessor 1991 ). Moreover, previous studies suggest that students with low self-esteem demonstrate more PSMU and CBP (Andreassen et al. 2017 ; Fan et al. 2016 ). Therefore, it is hypothesized that self-esteem will be negatively associated with CBP and PSMU. Moreover, moving from the vulnerability model indicating that self-esteem is a strong predictor to onset and maintenance of depression (Orth et al. 2008 ), it is hypothesized that depression will mediate the association of self-esteem with PSMU and CBP.

Social Connectedness and Belongingness

Many Internet behaviors are social behaviors. Research has shown that factors such as cohesive family environment and caring adults—which are considered among the social and perceived environmental dimensions of the PBT (Jessor 1991 )—decrease the engagement in problem behaviors (e.g., O’Connor et al. 2016 ). Some of the social needs that cannot be met in everyday life such as social connectedness (SC) and general belonging (GB) can be met in the virtual environment via social media platforms, and this may affect the frequency of social media use. SC was defined by Lee and Robbins ( 1995 ) as a sense of belonging that individuals feel towards their peers and society they live in and is derived from one’s relationship experiences with one’s surroundings. GB was defined by Hagerty et al. ( 1992 ) as “the experience of personal involvement in a system or environment so that persons feel themselves to be an integral part of that system or environment” (p. 173). SC and GB are considered as some of the most important social needs for individuals to build up and maintain relationships (e.g., Hagerty et al. 1996 ; Lee and Robbins 1995 ). Previous studies found that individuals who grow up with the feeling of low SC become outsiders and disconnected from society (Lee and Robbins 2000 ). Such individuals are also likely to have low self-esteem, build unsatisfactory relationships, and lack social identity (Lee and Robbins 1998 ). SC, which is negatively associated with loneliness, social discomfort, social distress, and depression (Lee et al. 2001 ), plays a vital role on adolescents’ lives and psychosocial development. Individuals, particularly during adolescence, need and search to connect to their peers and loved ones (Wu et al. 2016 ). Students with higher SC report more subjective wellbeing (Jose et al. 2012 ) and maintain a healthier development. Moreover, lower GB is related to many negative factors that are related to psychological wellbeing of students, such as lower SC, happiness, and life satisfaction, as well as higher loneliness, depression, avoidant and ambivalent attachment styles, neuroticism, and introversion (Malone et al. 2012 ).

Being online in social media platforms, which is perceived as a type of socialization, can provide individuals with the feeling of belonging (Yıldırım 2014 ). Individuals’ impetus to seek out long-term satisfying relationships can be a motivation for their emotions, thoughts, and behaviors. Because short-term relationships do not satisfy human beings in the long run, individuals seek out relationships that meet their need of belonging (Baumeister and Leary 1995 ). Individuals, who have the need to live in a community, may also satisfy their need for a safe and predictable community environment via social media sites (Griffiths et al. 2014 ). In other words, in case of individuals who have few social connections in their real life, the Internet is the place where they can coexist with other people and restore their damaged social self (Eroğlu 2016 ). Meeting new people via social media and building new friendships make individuals feel less lonely and more supported (Shaw and Gant 2002 ).

Online relationships are often regarded as closer, more secure, and less intimidating than real-life friendships, and they can reduce the perceived loneliness in the lives of the addicts (Young 1998 ). Kim et al. ( 2013 ) reported that problematic Internet use was three times more prevalent among students at the end of an 18-month period compared to their first days at university, and that this increase was due to psychosocial factors such as depression and life dissatisfaction. In other words, an individual typically becomes a problematic social media user in order to cope with the real-life problems. However, Ellison et al. ( 2007 ) demonstrated that the friendships created via social media platforms are not close friendships. There are also studies showing that social media use predicts perceived social support and happiness in a negative way (Çolak and Doğan 2016 ; Moody 2001 ; Sum et al. 2008 ), although there are some studies suggesting that individuals use social media platforms to meet their social support needs (Amichai-Hamburger and Ben-Artzi 2003 ). The present study used the separate constructs of social connectedness and general belongingness with two different samples (i) because the social connectedness assessment tool that was used in the study was developed in order to assess adolescents’ levels of social connectedness while general belongingness scale was developed for individuals that are aged 18 years and above, and (ii) despite the fact that they appear to be different concepts, social connectedness and general belongingness overlap and are very strongly correlated (Malone et al. 2012 ). Thus, the present study investigated the variation of these concepts’ relationships among two different samples by offering two separate complex models.

The Present Studies

The present study offered a complex mediaton model with the variables that were previously shown to be related. This was in order to bring a more detailed explanation concerning the effects of these variables, and to include some variables that have not previously been empirically shown to be associated. Several hypotheses were constructed from the aforementioned studies. Based on recent previous studies (Andreassen et al. 2017 ; Kırcaburun and Tosuntaş 2017 ), it is hypothesized that females will demonstrate less CBP and more PSMU. Based on previous studies (Andreassen et al. 2017 ; Ševčíková and Šmahel 2009 ), it is also hypothesized that younger students will be more problematic social media users and cyberbullying perpetrators. Based on PBT and existing literature, it is hypothesized that depression and low self-esteem will be positively associated with CBP and PSMU, and depression will mediate the association of self-esteem with PSMU and CBP. Utilizing PBT (Jessor 1991 ) and previous studies associating loneliness with PSMU (Griffiths et al. 2014 ), it is hypothesized that students with lower sense of SC and GB will report higher PSMU and CBP. Moreover, since decreased feelings of SC and GB have been reported to increase depressive symptoms (Lee et al. 2001 ; Malone et al. 2012 ), it is hypothesized that depression will mediate the effect of SC and GB on CBP and PSMU (see Fig.  1 ).

Hypothesized model

Method (Study 1): Relationships between CBP, PSMU, SC, Depression, and Self-Esteem

The goal of study 1 was to examine the relationships between CBP, PSMU, SC, depression, and self-esteem among high school students using a structural equation model (see Fig. 1 for the hypothesized model).

Participants

A total of 1143 students participated in study 1. In the first step, 339 high school and university students (29% female; M age  = 17.96 years, SD = 2.46) were used for adaptation of the Cyberbullying Offending Scale (CBOS) and the Short Depression-Happiness Scale (SDHS) into Turkish. In the second step, a convenience sample of 804 students aged between 14 and 21 years from five different high schools (48% female; M age  = 16.20 years, SD = 1.03), filled out the questionnaires. Researchers gathered the data by visiting each school, giving the necessary information, and handing out the questionnaires to students. Students participated in the study voluntarily and anonymously.

Cyberbullying Offending Scale (CBOS)

The CBOS assesses the level of different types of cyberbullying perpetration behaviors, comprising nine items (e.g., “I spread rumors about someone online”) on a five-point Likert scale from “never” to “several times.” It was developed by Patchin and Hinduja ( 2015 ) and adapted to Turkish by the researchers. After introducing the error covariances in the model, suggested by the modification indices, confirmatory factor analysis (CFA) of CBOS generated acceptable fit values for the unidimensional factor structure (χ 2 /df = 2.86, RMSEA = 0.07 (CI 90% (0.05, 0.10)), SRMR = 0.03, CFI = 0.98, NFI = 0.97, GFI = 0.96, IFI = 0.98). The Cronbach’s alpha in the Turkish validation of the scale was 0.89. Analyses indicated that Turkish form of the CBOS was valid and reliable for assessing high school and university students’ cyberbullying perpetration levels. Cronbach’s alpha of the scale in study 1 was 0.87.

Social Media Use Questionnaire (SMUQ)

The SMUQ scale assesses problematic and excessive use of social media, comprising nine items (e.g., “I feel anxious, when I am not able to check my social network account”) on a five-point Likert scale from “never” to “always,” with two factors which are withdrawal and compulsion. It was developed by Xanidis and Brignell ( 2016 ) and adapted to Turkish in a previous study (Kircaburun et al. 2018 ). The Cronbach’s alpha for the total scale in validation of the Turkish form was 0.90. In study 1, Cronbach’s alpha of the total scale was 0.83.

Social Connectedness Scale (SCS)

The SCS assesses feeling of belonging among adolescents aged between 14 and 18 years (Lee and Robbins 1995 ). It comprises eight items (e.g., “I do not feel that I participate with anyone or any group”) on a six-point Likert scale ranging from “absolutely disagree” to “absolutely agree.” It was adapted to Turkish by Duru ( 2007 ). Reported Cronbach’s alpha for the original Turkish validation was 0.90, and in the present study it was 0.89.

Short Depression-Happiness Scale (SDHS)

The original SDHS was developed by Joseph et al. ( 2004 ). CFA was used to test the proposed one-factor structure of the scale on the Turkish sample. However, the analysis provided poor fit indices. Therefore, exploratory factor analysis (EFA) was carried out. According to the EFA, the Turkish form of the scale comprised of two sub-scales, depression and happiness, with three items each, on a four-point Likert scale from “never” to “often.” Item factor loadings were 0.79, 0.80, and 0.84 for the depression sub-scale and 0.85, 0.87, and 0.89 for the happiness sub-scale. Total variance explained by the total scale was 70.71%. CFA confirmed the two dimensional structure with a good fit (χ 2 /df = 1.65, RMSEA = 0.04 (CI 90% (0.00, 0.08)), SRMR = 0.05, CFI = 0.99, NFI = 0.98, GFI = 0.99, IFI = 0.99). For the present study, only the depression sub-scale (e.g., “I felt that life was meaningless”) was used to assess depression levels among participants. Cronbach’s alpha found in study 1 for the depression sub-scale was 0.75.

Single-Item Self-Esteem Scale (SISE)

The SISE comprises one item (“I have a high self-esteem”) on a seven-point Likert scale from “absolutely incorrect” to “absolutely correct.” It was developed by Robins et al. ( 2001 ). Given the SISE only has one item, the Cronbach’s alpha coefficient of the scale cannot be calculated. However, the original study reported that the SISE had strong convergent validity with Rosenberg’s Self-Esteem Scale (Rosenberg 1965 ) and it has been used by many researchers around the world in order to assess self-esteem levels of participants.

Statistical Analysis

In order to show the relationships between variables descriptive, Pearson correlations and path analyses were conducted via using the SPSS 23.0 and AMOS 23.0 software applications. Normality assumptions were checked by examining the skewness and kurtosis values of the variables. Since skewness values were smaller than |3| and kurtosis values were smaller than |10| (Kline 2004 ), normal distribution was accepted. In the path analysis, the maximum likelihood estimation method was used. Furthermore, goodness of fit indices designated by Hu and Bentler ( 1999 ) were utilized. Consequently, thresholds for good and acceptable fit values are as follows: Root Mean Square Residuals (RMSEA) < 0.05 is good, Standardized Root Mean Square Residuals (SRMR) < 0.05 is good, Goodness of Fit Index (GFI) > 0.95 is good, Comparative Fit Index (CFI) > 0.95 is good, Normed Fit Index (NFI) > 0.95 is good, and Incremental Fit Index (IFI) > 0.95 is good; also RMSEA < 0.08 is acceptable, SRMR < 0.08 is acceptable, GFI > 0.90 is acceptable, CFI > 0.90 is acceptable, NFI > 0.90 is acceptable, and IFI > 0.90 is acceptable. Path analyses were carried out via using bootstrapping method with 95% bias-corrected confidence intervals and 5000 bootstrapped samples.

Descriptive statistics and bivariate correlations of all study variables are presented in Table 1 . CBP was moderately correlated with PSMU ( r  = 0.33, p  < 0.001) and weakly with depression ( r  = 0.19, p  < 0.001), self-esteem ( r  = − 0.19, p  < 0.001), and SC ( r  = − 0.17, p  < 0.001). PSMU was moderately correlated with depression ( r  = 0.37, p  < 0.001) and weakly with SC ( r  = − 0.20, p  < 0.001) and self-esteem ( r  = − 0.15, p  < 0.001). Finally, depression was moderately correlated with SC ( r  = − 0.39, p  < 0.001) and self-esteem ( r  = − 0.27, p  < 0.001).

In order to demonstrate the significant direct and indirect relationships between variables, path analysis was applied. Path analysis showed that PSMU ( r  = 0.30, p  < 0.001; 95% CI (0.27, 0.39)), self-esteem ( r  = − 0.15, p  < 0.001; 95% CI (− 0.23, − 0.06)), and gender ( r  = 0.21, p  < 0.001; 95% CI (0.15, 0.27)) were directly related to CBP. SC ( r  = − 0.04, p  < 0.001; 95% CI (− 0.05, − 0.03)) and self-esteem ( r  = − 0.02, p  < 0.001; 95% CI (− 0.03, − 0.01)) were indirectly related to CBP through depression and PSMU while depression ( r  = 0.10, p  < 0.001; 95% CI (0.07, 0.13)) was indirectly related to CBP via PSMU. Moreover, depression ( r  = 0.32, p  < 0.001; 95% CI (.24, 0.39)) and gender ( r  = − 0.07, p  < 0.001; 95% CI (− 0.13, − 0.01)) were directly related to PSMU, while SC ( r  = − 0.10, p  < 0.001; 95% CI (− 0.14, − 0.08)) and self-esteem ( r  = − 0.04, p  < 0.001; 95% CI (− 0.07, − 0.03)) were indirectly related to PSMU via depression. While depression mediated the relationships of self-esteem (fully) and SC (fully) with PSMU, PSMU mediated the association of depression (fully) with CBP. Standardized estimates of total, direct, and indirect effects on PSMU and mediator variables and cyberbullying perpetration and mediator variables, respectively, are summarized in Table 2 . The model predicted 22% of the depression, 14% of the PSMU, and 18% of the CBP (Fig.  2 ).

Final model of the significant path coefficients of study 1. For clarity, the correlations between the four independent variables have not been depicted in the figure

Method (Study 2): Relationships between CBP, PSMU, GB, Depression, and Self-Esteem

The goal of study 2 was to examine the relationships between PSMU, CBP, GB, depression, and self-esteem among university students using a structural equation model (see Fig.  1 for the hypothesized model).

In study 2, a convenience sample of 760 students from a state university, aged between 18 and 40 years (60% female; M age  = 21.48 years, SD = 3.73), filled out the questionnaires voluntarily and anonymously. Researchers gathered the data by visiting each class, giving the necessary information and handing out the questionnaires to students.

The same measures used in study 1 were also used in study 2 apart from the addition of the General Belongingness Scale (GBS) instead of the Social Connectedness Scale (SCS). The GBS assesses feeling of belonging levels of university students, comprising 12 items (e.g. “I feel connected with others.”) on a seven-point Likert from “absolutely disagree” to “absolutely agree.” It was developed by Malone et al. ( 2012 ) and adapted into Turkish by Duru ( 2015 ) and reported optimal validity and reliability. In the present study, the scale showed high internal consistency with a Cronbach’s alpha of 0.88. The Cronbach alphas for the scales in study 2 were 0.83 (CBOS), 0.85 (SMUQ), and 0.77 for the depression sub-scale of the SDHS.

Procedure and Statistical Analysis

Information regarding the procedure and statistical analysis in study 2 was the same as study 1.

According to Pearson’s correlation analysis (Table 3 ), CBP was weakly correlated with gender ( r  = 0.20, p  < 0.001), GB ( r  = − 0.18, p  < 0.001), PSMU ( r  = 0.13, p  < 0.001), depression ( r  = 0.11, p  < 0.01), age ( r  = − 0.09, p  < 0.05), and self-esteem ( r  = − 0.09, p  < 0.05). Moreover, PSMU was weakly correlated with depression ( r  = 0.22, p  < 0.001), gender ( r  = − 0.15, p  < 0.001), age ( r  = − 0.13, p  < 0.001), GB ( r  = − 0.10, p  < 0.001), and self-esteem ( r  = − 0.11, p  < 0.01). Finally, depression was moderately correlated with GB ( r  = − 0.35, p  < 0.001) and self-esteem ( r  = − 0.26, p  < 0.001).

In order to show the significant direct and indirect relationships between variables, path analysis was applied. Path analysis showed that gender ( r  = 0.20, p  < 0.001; 95% CI (0.12, 0.27)), age ( r  = − 0.08, p  < 0.001; 95% CI (− 0.13, − 0.03)), GB (r  = − 0.10, p  < 0.05; 95% CI (− 0.19, − 0.00)), and PSMU ( r  = 0.12, p  < 0.01; 95% CI (0.04, 0.20)) were directly related to CBP. Depression ( r  = 0.02, p  < 0.01; 95% CI (0.01, 0.04)) and GB ( r  = − 0.03, p  < 0.01; 95% CI (− 0.06, − 0.01)) were indirectly related to CBP via PSMU. Moreover, gender ( r  = − 0.17, p  < 0.001; 95% CI (− 0.24, − 0.11)), age ( r  = − 0.09, p  < 0.01; 95% CI (− 0.15, − 0.02)), GB ( r  = − 0.13, p  < 0.001; 95% CI (− 0.20, − 0.05)), and depression ( r  = 0.17, p  < 0.001; 95% CI (0.09, − 0.24)) were directly related to PSMU, while GB ( r  = − 0.05, p  < 0.001; 95% CI (− 0.08, − 0.03)) and self-esteem ( r  = − 0.02, p  < 0.001; 95% CI (− 0.04, − 0.01)) were indirectly related to PSMU via depression. While depression mediated the relationships of self-esteem (fully) and GB (partially) with PSMU, PSMU mediated the associations of GB (partially) and depression (fully) with CBP. Standardized estimates of total, direct, and indirect effects on PSMU and mediator variables and cyberbullying perpetration and mediator variables, respectively, are summarized in Table 4 . The model predicted 14% of the depression, 10% of the PSMU, and 9% of the CBP (Fig.  3 ).

Final model of the significant path coefficients of study 2. For clarity, the correlations between the four independent variables have not been depicted in the figure

The present study used the problem behavior theory (PBT) framework to examine among high school and university students how cyberbullying perpetration (CBP) and problematic social media use (PSMU) are associated with each other and to gender, age, depression, self-esteem, self connectedness (SC), and general belongingness (GB). According to model results, which were partially in line with the PBT, PSMU was (i) directly related to being female, being younger (only among university students sample), GB, and depression, and (ii) indirectly to self-esteem, SC, and GB via depression. Furthermore, CBP was (i) directly related to being male, being younger, GB, and PSMU, and (ii) indirectly to being male, being younger, GB, and depression via PSMU. Nevertheless, it should be emphasized that the majority of these associations, although significant, were relatively weak. Therefore, other factors—not included in the present study—are likely to play an important role in PSMU and CBP and need to be investigated in future studies.

As expected, adolescents and young adults who demonstrated higher PSMU also showed higher CBP. This finding concurs with previous studies reporting that online problem behaviors such as problematic and addictive use of Internet and CBP are related (e.g., Casas et al. 2013 ; Eksi 2012 ; Gámez-Guadix et al. 2016 ; Kırcaburun and Baştug 2016 ) and is also consistent with PBT (Jessor 1991 ). When all these studies are considered together, there is supporting evidence that PBT is able to explain the risky online and offline behaviors of students. This finding is important because it presents evidence that lowering PSMU may be used as an intervention method in order to prevent students from engaging in CBP. Although the results of the present study suggests that university students’ problem online behaviors may be explained by the PBT, it should also be emphasized that, as expected, adolescent students’ CBP was related more strongly with their PSMU when compared to university students. However, this may be due to the fact that PBT was originally proposed for adolescents.

Regarding gender, and as hypothesized, female students had more PSMU and less CBP in both samples. These results are consistent with previous studies (e.g., Andreassen et al. 2017 ; Festl and Quandt 2013 ; Kırcaburun and Tosuntaş 2017 ; Kokkinos et al. 2014 ; Slonje et al. 2012 ). Andreassen et al. ( 2013 ) reported that females demonstrated higher addictive behaviors among activities including social interactions. Randler et al. ( 2016 ) reported that females were more addicted to smartphones than males. Social media platforms, which facilitate social interactions, acquaintances and friendships, are easily accessible via smartphones. On the other hand, male students were more likely to be cyberbullying perpetrators, which may be explained by the fact that they are also more likely to be cyberbullying victims (Kokkinos et al. 2014 ). Cyberbullying victimization has been reported to be one of the strong predictors of CBP both directly and indirectly via anger (Ak et al. 2015 ). Moreover, Finigan-Carr et al. ( 2016 ) suggest that there are significant gender differences in types of aggressive behaviors, and that male gender is more strongly associated with aggressive and violent behaviors compared to females.

Partially in line with the hypothesis, age was negatively related to PSMU and CBP among university students but was not significant among adolescent high school students. These findings are in line with some studies (Andreassen et al. 2017 ; Cho and Yoo 2017 ) but contradict the findings of others (Hinduja and Patchin 2014 ). The inconsistent results may perhaps be explained by the age range of the samples used in the two studies. For instance, the high school sample was aged between 14 and 21 years, whereas the university students’ ages ranged from 18 to 40 years.

The hypothesis regarding depression was partially supported. The strongest direct predictor of PSMU was depression and it was more strongly related to PSMU among high school students. This result is in line with the study of Shensa et al. ( 2017 ) which indicated that PSMU is strongly associated with increased depressive symptoms. Adolescence is a psychologically more fragile period for individuals in which depression is more commonly seen among students (Riglin et al. 2016 ) and is more positively associated with antisocial behaviors (Choi et al. 2016 ). Students with higher depression may be trying to cope with their vulnerable and volatile psychological states by logging online and by spending excessive time engaged in social media use. Moreover, the present study demonstrates for the first time that the association between depression and CBP was fully mediated by PSMU among both adolescents and young adults. Higher depression levels among students were associated with higher PSMU, and higher PSMU was related to higher CBP. Additionally, the present study contributes to further understanding of the relationships between self-esteem, SC, and GB with PSMU. Students with lower sense of social connectedness, belongingness, and self-esteem had higher levels of depression, which in turn, was related to higher PSMU. This result is consistent with the PBT framework of the study, which suggests that students’ problem behaviors are dependent on many interrelated risk and protective factors such as individuals’ personality, other psychological factors, and interactions with their social environments (Jessor 1991 ).

Finally, and partially in line with the hypothesis , SC was indirectly related to PSMU and CBP via depression. Furthermore, GB was found to be a significant direct and indirect predictor of both PSMU and CBP. Among young adults, students who were feeling more rejected by and isolated from their surroundings used social media more excessively and perpetrated more cyberbullying. However, adolescent students who were feeling less socially connected to their surroundings had higher levels of depression, which in turn were related to increased PSMU and CBP. According to PBT, students who have healthy and positive social relationships with their schools, families, and environmental surroundings are expected to develop and maintain fewer problematic and risky behaviors (Jessor 1991 ). This may also be interpreted by the negative strong correlation of GB with loneliness (Malone et al. 2012 ), in which higher loneliness is also reported to be positively related to PSMU (Griffiths et al. 2014 ). Students who feel socially isolated may spend excessive time on social media in order to compensate their need for socializing and feeling connected (Ahn and Shin 2013 ) and demonstrate antisocial online behaviors if they also felt rejected in online contexts.

The present study has several limitations. First, the data were collected by using self-report questionnaires. In future studies, mixed methods should be used for deeper understanding on the relationships of the variables. Second, since the nature of the study was cross-sectional, causal assumptions from the results cannot be concluded. Third, due to convenience sampling, results of this study represent only the sample of the study; therefore, more representative age and ethnic groups should be targeted in future studies for broader generalization of the findings. Finally, the majority of these associations, although significant, were relatively weak. Therefore, other factors—not included in the present study—may also play an important role in PSMU and CBP. Despite its limitations, the present study posited important new associations in understanding problematic and risky online behaviors among adolescents and young adults. In sum, the present research indicates that, consistent with PBT, students’ problematic social media use is positively associated with their cyberbullying perpetration behaviors. PSMU is indirectly associated with lower social connectedness and self-esteem and directly with belongingness, depression, and being female. Furthermore, CBP is (i) indirectly associated with social connectedness, belongingness, and depression and (ii) directly with being male, belongingness, and lower self-esteem. Overall, the study highlights the important influence of depression on problematic online behaviors among students.

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Zsolt Demetrovics was supported by the Hungarian National Research, Development and Innovation Office (Grant number: K111938, KKP126835). Orsolya Király was supported by the New National Excellence Program of the Ministry of Human Capacities. The funding organization had no role in the design or conduct of the study or the collection, management, analysis, or interpretation of the data or the preparation, review, or approval of the article.

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Kırcaburun, K., Kokkinos, C.M., Demetrovics, Z. et al. Problematic Online Behaviors among Adolescents and Emerging Adults: Associations between Cyberbullying Perpetration, Problematic Social Media Use, and Psychosocial Factors. Int J Ment Health Addiction 17 , 891–908 (2019). https://doi.org/10.1007/s11469-018-9894-8

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Online Social Networking and Mental Health

During the past decade, online social networking has caused profound changes in the way people communicate and interact. It is unclear, however, whether some of these changes may affect certain normal aspects of human behavior and cause psychiatric disorders. Several studies have indicated that the prolonged use of social networking sites (SNS), such as Facebook, may be related to signs and symptoms of depression. In addition, some authors have indicated that certain SNS activities might be associated with low self-esteem, especially in children and adolescents. Other studies have presented opposite results in terms of positive impact of social networking on self-esteem. The relationship between SNS use and mental problems to this day remains controversial, and research on this issue is faced with numerous challenges. This concise review focuses on the recent findings regarding the suggested connection between SNS and mental health issues such as depressive symptoms, changes in self-esteem, and Internet addiction.

Introduction

D uring the past 10 years , the rapid development of social networking sites (SNSs) such as Facebook, Twitter, MySpace, and so on has caused several profound changes in the way people communicate and interact. Facebook, as the biggest social networking Web site, today has more than one billion active users, and it is estimated that in the future, this number will significantly increase, especially in developing countries. Facebook is used for both business and personal communication, and its application has brought numerous advantages in terms of increasing connectivity, sharing ideas, and online learning.

Recently, however, some researchers have associated online social networking with several psychiatric disorders, including depressive symptoms, anxiety, and low self-esteem. Since social networks are a relatively new phenomenon, many questions regarding their potential impact on mental health remain unanswered. On the other hand, due to the popularity of these online services in the general population, any future confirmed connection between them and psychiatric diseases would pose a serious public health concern.

This concise review focuses on the recent research regarding the suggested connection between social networking and depressive symptoms, changes in self-esteem, and other potential psychiatric problems and issues. The articles cited in this text were selected from the Web of Science citation indexing database (Thomson Reuters) using KoBSON search tool (Konzorcijum biblioteka Srbije za objedinjenu nabavku; Serbian Library Consortium for Coordinated Acquisition). The search was conducted using a total of 50 different keywords related to social networking and mental health, such as “Facebook,” “Twitter,” “Depression,” “Addiction,” “self-esteem,” and so on. The priority was given to the articles published during the past 10 years in the journals with high 2 year and 5 year impact factors (the upper 50% rank in the journal category), as well as to the articles with higher number of citations. The number of citations for selected article was determined using the Elsevier Scopus database.

Facebook and Symptoms of Depression

Although several studies have made the connection between computer-mediated communication and signs and symptoms of depression, this issue remains controversial in current psychiatry research. There are many potential reasons why a Facebook user may have a tendency to become depressed, as there are numerous factors that may lead an already depressed individual to start to use or increase their use of SNS.

In 1998, Kraut et al. published one of the first studies to indicate that Internet use in general significantly affects social relationships and participation in community life. 1 In this research, the authors found that increased time spent online is related to a decline in communication with family members, as well as the reduction of the Internet user's social circle, which may further lead to increased feelings of depression and loneliness. This work was later followed by several other publications where it was suggested that computer use may have negative effects on children's social development. 2

At the time when these studies were conducted, most of today's social networks did not exist. For example, Facebook was founded in 2004, and became popular among children and adolescents a few years later. Instead, most works were focused on the investigation of possible effects of Internet browsing, e-mail checking, and other online and offline behaviors (i.e., violent video games) on mental health.

With the development of social networks, the time children and adolescents spend in front of the computer screens has significantly increased. This has led to the further reduction of intensity of interpersonal communication both in the family and in the wider social environment. Although social networks enable an individual to interact with a large number of people, these interactions are shallow and cannot adequately replace everyday face-to-face communication.

Since social networks are a relatively recent phenomenon, this potential relationship between their use and feelings of loneliness and depression has not yet been properly investigated. Most of the research on this issue has been published during the past few years, and so far, the scientific community has not been able to interpret and discuss the results fully.

In our recent study in a high school student population, we found a statistically significant positive correlation between depressive symptoms and time spent on SNS. 3 Depression symptoms were quantified using the Beck Depression Inventory (BDI-II). On the other hand, no such correlation was detected between BDI score and time spent watching television. Other authors have reported that there is no relationship between SNS and depressive symptoms in a sample of older adolescents—university students using the Patient Health Questionnaire-9 depression screen. 4 Apart from differences in applied methodology, there is a possibility that different age groups (i.e., high school children vs. older adolescents) may react differently to SNS content and challenges. 5

In 2013, Kross et al. published a study on the relationship between Facebook use and subjective well-being in young adults. 6 The design of this research was based on text messaging the participants five times per day for 2 weeks in order to evaluate their mood, feeling of loneliness, social interactions, and social Facebook use. This approach was combined with the application of a conventional set of questionnaires, such as the Beck Depression Inventory, Rosenberg Self-Esteem Scale, Social Provision Scale, and Revised UCLA Loneliness Scale. 6 The results indicated that users' subjective perception of well-being and life satisfaction may be undermined. It goes without saying that any decline of this sort may increase depressive signs and symptoms.

One of the reasons why time spent on SNS may be associated with depressive symptoms is the fact that computer-mediated communication may lead to the altered (and often wrong) impression of the physical and personality traits of other users. This may lead to incorrect conclusions regarding physical appearance, educational level, intelligence, moral integrity, as well as many other characteristics of online friends. Recently, Chou and Edge published an article about the potential impact of using Facebook on students' perceptions of others' lives. The study carried out on 425 undergraduate students of at a state university in Utah reported that Facebook use is linked to participants' impression that other users are happier, as well as the feeling that the “life is not fair.” 7 Perceiving others as happier and more successful does not necessarily result in depression. However, in individuals who already have certain depressive predispositions as well as other psychiatric comorbidities, this may further negatively impact mental health.

As it is thought that Facebook may be one of the factors influencing the development of depressive symptoms, it is also assumed that certain characteristics of online behavioral may be predictive factors in depression identification and assessment. Today, it is clear that SNS such as Facebook can be useful in the early detection of depression symptoms among users. Recently, Park et al. published a study in which they suggested that the more depressive the user is, the more he/she would use Facebook features that focus on depression tips and facts. The authors designed a unique application —EmotionDiary—that was proven to be capable of evaluating symptoms of depression in individuals. 8 In other words, certain depressive behavioral characteristics of a social network user can be quantified, and that quantification has a potentially high predictive value for a future diagnosis of depression. Apart from these results, this work also presented some evidence that a depressed Facebook user has other characteristics, such as a fewer friends and location tagging. Since these traits can be quantifiable, they could also be valuable predictors for possible future depression screening.

However, it should be stressed that there is still no conclusive evidence that use of Facebook and other SNS causes depression or even a single symptom of depression. Kraut et al., the authors of the above-mentioned study on Internet and depression, recently published results indicating that online communication with friends and family (today mostly done on SNS) is actually associated with a decline in depression. 9 It seems that when social networks and the Internet in general are used to strengthen and maintain social ties, particularly within family members and close friends, the resulting social support has beneficial effects on mental health. On the other hand, extensive use of SNS outside these circles might weaken existing close family and friend interactions and increase feelings of loneliness and depression.

Social Networking and Self-Esteem

Many authors define the term “self-esteem” as “the evaluative component of the self—the degree to which one prizes, values, approves or likes oneself.” 10 , 11 It is an important factor in developing and maintaining mental health and overall quality of life. 12–14 Low self-esteem is associated with the pathogenesis of numerous mental illnesses, including depression, eating disorders, and addiction. 15–22 Recent studies have presented conflicting results regarding the potential influence of Facebook and other SNS on self-esteem.

One of the possible explanations regarding the negative relationship between Facebook and self-esteem is that all social networking platforms where self-presentation is the principal user activity cause or at least promote narcissistic behavior. 23–27 A report by Mehdizadeh described the findings of a study in which 100 Facebook users at York University provided self-esteem and narcissistic personality self-reports. The results indicated that individuals with lower self-esteem are more active online in terms of having more self-promotional content on their SNS profiles. In other words, certain Facebook activities (such as “The Main Photo” feature) were negatively correlated with self-esteem measured with the Rosenberg Self-Esteem Scale. 23

On the other hand, some authors have presented results indicating that Facebook use may actually enhance self-esteem. A study by Gonzales and Hancock included groups of student participants exposed to three different settings: exposure to a mirror, exposure to one's own Facebook profile, and a control setting. The level of self-esteem in all participants was estimated using the Rosenberg Self-Esteem Scale. The results showed the positive effects of Facebook on self-esteem supporting the so-called Hyperpersonal Model in which selective self-presentation positively impacts impressions of the self. 28

According to data from recent literature, as well as the above-mentioned research, there are indeed several models/theories on the possible effect of computer-mediated communication on self-esteem in the general population. Objective self-awareness theory 29 suggests that any stimulus causing the self to become the object (instead the subject) of the consciousness will lead to a diminished impression of the self. These stimuli include looking at oneself in a mirror, hearing one's own voice, writing one's own curriculum vitae, or any other situation during which the subject's attention focuses on the self. 28 It is probable that a typical Facebook user will every day have multiple visits to his/her own profile page during which he will view his already posted photographs, biographical data, relationship status, and so on. All of these events, especially in light of similar data obtained from other users' profiles, may lead to either a short-term or a long-term reduction in self-esteem.

The “hyperpersonal model” of behavior during computer-mediated communication, mentioned in the study by Gonzales and Hancock, is also one of the possible factors that can modulate the self-esteem of a Facebook user. This model stresses the advantages of computer-mediated communication over conventional face-to-face communication in terms of users being able to optimize self-presentation to others more effectively. 28 In fact, it is suggested that when using an online platform, the subject has more time to select, emphasize, and present those aspects of his/her personality, character, and temperament that would be viewed more favorably by the receivers or, in this case, other Facebook users. This is in contrast to conventional face-to-face interaction where the subject does not have enough time and opportunity to present the positive features of himself selectively. Based on this model, we could assume that this selective self-presentation on a SNS and increased relationship formation would impact positively on self-evaluation and therefore self-esteem.

It is probable, however, that the overall impact of SNS on self-esteem is much more complex. Constant self-evaluation on an everyday basis, competition and comparing one's own achievements with those of other users, incorrectly perceiving physical/emotional/social characteristics of others, feeling of jealousy, and narcissistic behavior—these are all factors that may positively or negatively influence self-esteem. Unfortunately, despite several research efforts during the past decade, this issue still remains unresolved, and probably many years will pass before we comprehend the true nature of this relationship.

Online Social Network Addiction

Addiction to online social networking, as well as Internet addiction in general, are recent and insufficiently investigated phenomena, frequently discussed and sometimes disputed in the psychiatric literature. 30–35 The addictive nature of SNS is supported primarily by the mental preoccupation of many chronic SNS users who as a result tend to neglect other aspects of their social functioning such as family and offline friends. In addition, according to our own observations, sudden cessation of online social networking (i.e., lack of Internet connection) may in some chronic users cause signs and symptoms that at least partially resemble the ones seen during drug/alcohol/nicotine abstinence syndrome.

Online social networking as a potential addiction disorder has so far been discussed in many publications. 30 , 31 , 33 , 35–37 SNS addiction represents a relatively new issue in psychiatry research, and as with other potentially SNS-related disorders, many questions remain unanswered.

In 2012, Andreassen et al. developed the Facebook Addiction Scale, a scoring system initially based on a total of 18 items, testing features of addiction such as salience, mood modification, tolerance, withdrawal, conflict, and relapse. 37 The authors applied the scale along with other questionnaires (such as Addictive Tendencies Scale, Online Sociability Scale, etc.) on a sample of 423 students. The test showed a relatively high reliability and proved to be applicable to the student population. The same year, regarding this study, Griffiths 35 expressed concern that the term “Facebook addiction” may be obsolete due to a large variety of activities that can be done on Facebook besides conventional social networking (i.e., playing games). Nevertheless, any attempt to design a scoring system that would be able to quantify at least a certain aspect of social networking addiction is, in our opinion, an important addition to the present knowledge in this field.

Wolniczak et al. 38 recently adapted The Internet Addiction Questionnaire in order to test Facebook dependence in the student population. The authors also tested the sleep quality of Facebook users using the Pittsburgh Sleep Quality Index. The results showed that Facebook dependence may be related to poor quality of sleep. To our knowledge, this is the first study to modify existing questionnaires for Internet addiction in order to test Facebook use.

Probably, the most important question is whether SNS addiction is actually a mental disorder, and whether it should be diagnosed and treated as such. The Tenth Revision of the International Classification of Diseases and Health Problems (ICD-10) defined several specific criteria for dependence syndrome such as a strong desire or sense of compulsion, difficulties in controlling consumption behavior, physiological withdrawal state after reduction or cessation, evidence of tolerance, and so on. 39 A diagnosis should be made if three or more of the above-mentioned criteria are present (at a certain time point) during the previous year.

It is clear that many of these diagnostic criteria could be applied to a minor percentage of chronic Facebook users who, as a result of this prolonged computer use, have problems in normal everyday functioning. However, one must be very careful with this approach, since in the future it could be quite difficult to distinguish SNS addiction from Internet addiction, which is a much more general disorder (Internet addiction disorder, problematic Internet use, or compulsive Internet use). Furthermore, it should be noted that neither Internet nor SNS addiction have been included in the latest disease classification manuals such as Diagnostic and Statistical Manual of Mental Disorders (DSM-5). In addition, SNS and Internet-related mental problems are frequently seen together with other diagnosable mental illnesses, or, in other words, these problems are complicated by comorbidity. 34 Therefore, it remains unclear whether potential SNS addiction is an independent illness, or merely a manifestation of other mental issues such as, for example, personality disorders.

All in all, it remains to be seen whether SNS addiction will ever be recognized as a separate mental disorder. It can be expected that in the future, this issue will be a focal point of many research studies, and that, in the years to come, it will become the subject of a wide debate among psychiatrists, psychologists, and other specialists. The final results and conclusions will have a substantial impact on the future organization of the mental health system, particularly considering that online social networking affects such a large proportion of the world population.

Future Prospects

It can be expected that future research regarding the potential effects of online social networking on mental health is going to be faced with numerous difficulties. First, so far, many authors investigating this issue have used a cross-sectional study approach in their methodology, followed by correlation analysis. The existence of a correlation does not necessarily equal causality. For example, Facebook and self-esteem may be related in terms of Facebook usage, causing lower self-esteem, but this may also mean that people with low self-esteem use Facebook more often. In other words, it is very difficult, and sometimes impossible, to conclude which variable is the cause and which is the effect. In the future, longitudinal designs would be much more helpful in determining the effects of SNS use on mental health. Ultimately, the data obtained from experimental studies would enable us to draw definite conclusions on this relationship.

It should be noted that most of the research done so far on social networking and mental health was done on a healthy population (i.e., high school students, university students, adolescents in general). When it is stated that, for example, “time spent on social networking is related to depression,” the authors usually mean that this time correlates with physiological mood oscillations (measured with various psychological scales), rather than depression as a clinical entity. In fact, to our knowledge, no research of this sort has so far been conducted on psychiatric patients. Therefore, a possible connection between social networking and mental health issues can only be discussed in terms of normal physiological (psychophysiological) variations of psychic functions.

We should always have in mind that not all of the social networks are the same. The largest and most popular SNS, Facebook, is based on creating and updating personal profiles, where users can upload photos, videos, comments, statuses, and notes. Another popular SNS, Twitter, is based on a different concept: users post and read short text messages (“tweets”) in which they express their thoughts and opinions. Most of the studies mentioned in this text have been focused on Facebook as the predominant SNS, and even in the studies where authors in the title state the term “social networking,” in most cases, Facebook is the primary target of investigation. In fact, after searching the available scientific databases, we were unable to find a single study that was primarily focused on Twitter and its potential impact on mental health. In the future, it can be expected that Twitter will also become the subject of many research efforts.

Many studies simply do not test various potential confounding factors that may influence conventional correlation in terms of enhancing or reducing it. For example, it may well be possible that people with some personality disorders (which are quite frequent and often undiagnosed) spend much more time on online social networks compared to the general population because computer-mediated communication enables them to be socially more successful. These individuals, if included in a research study, will probably influence the results of self-esteem, depression, addiction, and other questionnaires. In other words, any future study on this topic, in order to meet quality standards, will need to have established precise inclusion and exclusion criteria in order to make the study sample as homogenous as possible. These criteria are often difficult to define and even more difficult to implement, so the other possible approach would be to use a large study sample. This would also have to be combined with additional statistical tests such as multivariate regression analysis.

Most of the research on social networking and mental health has so far been performed using conventional psychiatric questionnaires, such as the above-mentioned Rosenberg Self-Esteem Scale, the Beck Depression Inventory, and others. Today, it is not uncommon that for assessment of the same psychiatric sign/symptom, several different scales exist. For example, for quantification of depressive symptoms, the researcher may choose between scales such as the Beck Depression Inventory, the Centre for Epidemiological Studies—Depression Scale (CES-D), the Hamilton Rating Scale for Depression (HAM-D), the Zung Self-Rating Depression Scale, the Montgomery–Åsberg Depression Rating Scale (MADRS), and so on. Although these scales are established tools in psychology and psychiatry research, sometimes when designing a study, it is difficult to determine which scale has the best sensitivity for the given population/study sample. This may be especially the case when being used in the general population or in different age groups such as high school students, university students, and so on. In the future, there may be a need to design and implement novel, advanced scales that would be adjusted to evaluate potential mental problems in light of the rapid development of information technology, or at least to compare the existing ones in terms of establishing a set of recommendations for their application in these new conditions.

In conclusion, it is clear that during the past 10 years, online social networking has caused significant changes in the way people communicate and interact. It is unclear, however, whether some of these changes affect normal aspects of human behavior and cause psychiatric disorders. In the future, additional research will be needed to identify and describe the potential relationship between the use of SNS and various mental health issues.

Acknowledgments

The author is grateful to the Republic of Serbia, Ministry of Science and Education (Grants 175059 and 41027), as well as the Project 62013 of the DEGU Society, Belgrade, Serbia. The author also apologizes to all researchers in the fields of psychology, psychiatry, and social networking whose articles were not cited (unintentionally or due to the page limitations) in this work.

Author Disclosure Statement

No competing financial interests exist.

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Lesson plan booster: digital literacy and online ethics.

To students, the Internet can seem like a space where “anything goes.” People can seemingly say or post whatever they like, without any negative consequences. In the absence of face-to-face contact, young people in particular tend to lose their inhibitions and do or say things they would not in “real life.”

Grade Level:          8-12

Student learning objectives

Students will gain digital literacy (media literacy, information literacy) skills by considering the human and legal consequences of poor choices others have made online. Students will recognize that even when online expression is protected by the First Amendment, it still can result in legal consequences. Students also will discuss the concepts of “netiquette” and “cyber ethics” and then brainstorm a set of guidelines for appropriate online behavior.

Preparation

• You may want to provide the text of the First Amendment  to students either on paper or via electronic display.
• Acts of harassment are defined as written, oral, or physical acts that harm a student, damage the student’s property, interfere with the student’s education, or disrupt the orderly operation of a school. Categories of harassment are found in several federal statutes and prohibit discrimination based on gender; disability; and religion, race, color or national origin .
• Schools are obligated by law to prohibit harassment that occurs on school grounds and in some cases can prohibit off-campus action if it disrupts the learning environment of the harassed student. Victims of harassment are also likely to press criminal charges.
• The student expression is substantially disruptive . If school officials can reasonably predict that the student expression (even if made off of school grounds and not on a school-owned computer) will disrupt the learning environment or interfere with the rights of others in the school community (expressions that can be construed as bullying are a good example) then they can prohibit the expression, and it is not protected under the First Amendment.
• The student expression involves a true threat . If an expression communicates a serious, clear intent to harm someone — it receives no First Amendment protection and could result in a school ordering the “speaker” to halt the expression (or in the case of online material, delete the material). The expression could also result in criminal charges of threatening or even cyberstalking.
• Make sure you’re clear regarding one thing that’s often confusing for students: Even if speech is protected by the First Amendment, it still can result in criminal charges. Speech that is eligible for First Amendment protection is free from government interference; however, a person who feels harmed by someone’s speech may certainly file charges (such as criminal defamation or harassment) against the “speaker.” (You may want to note that laws vary by state in terms of the specific charges that can be filed in various situations.) Even if the accused is found not guilty, other negative consequences are likely to include a publicly announced arrest, embarrassing media stories and significant legal expenses.
• In the 2010 Roger Corey Bonsant case, Bonsant, then a 17-year-old high school student, was arrested and charged with criminal defamation after he was accused of creating a fake Facebook page using a teacher’s name and image. While the case is still being decided, this is an example of criminal ramifications that students may face for participating in dubious online acts.
• Several cases exist in which students who created false Facebook or MySpace pages featuring the names and likenesses of teachers and administrators. On these pages, students published items painting the educators as drug and sex addicts. In some cases school punishments were reversed by courts, due to the fact that the student activity took place off school grounds and presumably was not sufficiently “disruptive” of the school environment to override the students’ right to free speech. The victims depicted in these false Facebook pages could very well have filed charges, however.
• In 2011, a 12-year-old Seattle girl was arrested and charged with cyberstalking and first-degree computer trespassing . Authorities alleged that she stole a former friend’s Facebook password, logged into the account and posted explicit content. She was found guilty and sentenced to probation. (The girls’ school does not seem to have been involved in this case.)
• Six Nevada middle-schoolers were arrested in January, 2011 for using Facebook to invite other students to take part in “Attack a Teacher Day.” They were all arrested and charged with communicating threats , as several specific teachers were called out in posts to the Web site.
• In the Phoebe Prince case , Prince was bullied (both in person and online) by a group of teens at her Massachusetts high school after it was discovered she had a brief relationship with a boy. The boy’s girlfriend and a group of her friends systematically tormented Prince in retaliation. The bullying was considered a factor in Prince’s January 2010 suicide. All the teens involved were arrested on manslaughter charges. They eventually pled guilty to lesser crimes and were sentenced to probation and community service.
• Introduce the idea of “cyber ethics.” Professional journalists and publishers are held to a standard of ethics related to what they write and print (see Society of Professional Journalists Code of Ethics ). Consider whether some of the same ethical standards could, or should, apply to private citizens communicating on a public medium such as Facebook.
• Netiquette for Kids
• Netiquette Resource for Teens
• Tips to Help Stop Cyberbullying
• Thou shalt not use a computer to harm other people.
• Thou shalt not use a computer to bear false witness [lie].
• Thou shalt always use a computer in ways that ensure consideration and respect for your fellow humans.

Introducing discussion to students:  When students’ online behavior draws criticism from others, what kind of guidelines help us as a society determine what is acceptable in cyberspace? The First Amendment to the U.S. Constitution, federal and state laws, codes of “cyber ethics” and agreed-upon rules of “netiquette” can all play a role in shaping our behavior. We’re going to review some real cases of questionable student online behavior and see if we can learn from them about guidelines that might help us avoid problems.  

Options for student discussion or essay questions:

• Under what circumstances is students’ online “speech” protected by the First Amendment?
• Under what circumstances is online speech not protected (i.e., a school can order a student to stop, and issue punishment)?
• Can even protected online speech result in criminal charges? Under what circumstances? What kind of criminal charges can result?
• Even if online speech is legal (protected by The First Amendment), do individuals have a separate, personal ethical obligation to avoid certain online behaviors? If so, what kinds of things should not be done online?
• What lessons do you take away from the five student criminal cases that we discussed?
• Accused student(s)
• Parents of accused student(s)
• Classmates of accused student(s)
• Adult or youth victim(s) of students’ online behavior
• Law enforcement
• General public
• Do you think the judge made the right decision in the Phoebe Prince case, the Seattle cyberstalking case and the cases mentioned in the Star-Telegram editorial? Why or why not?
• Do important people in your life talk with you about the dangers of certain online behavior?
• Do young people fully understand the consequences of their online activity? Why or why not?
• What did you gain from reading others’ lists of “netiquette” rules? Which rules did you like/not like? Which rules do you think young people your age most often don’t follow?
• Which of these rules do you personally follow? Do you plan to change anything you’re currently doing online based on these guidelines (this could include not supporting others whose online behavior concerns you)? Are there other guidelines you would recommend adding to these lists?
• ( If teachers want to pair up or group students ) Did you agree with your partner/group about which rules should be on the list? Did you decide to add new rules?
• What can we do in our school and community to encourage students to follow these ethical guidelines?

Related resources

Access Denied: Blocked Web Sites and Schools Lesson Plan Booster: Think Before You Hit Send Do Texting and Facebook Belong in the Classroom?

Article by Jason Tomaszewski , EducationWorld Associate Editor Education World ®     Copyright © 2011 Education World

Updated 01/13/2015

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Sign up for our free weekly newsletter and receive top education news, lesson ideas, teaching tips and more! No thanks, I don't need to stay current on what works in education! COPYRIGHT 1996-2016 BY EDUCATION WORLD, INC. ALL RIGHTS RESERVED. COPYRIGHT 1996 - 2024 BY EDUCATION WORLD, INC. ALL RIGHTS RESERVED. SchoolNotes.com The Educator's Network Online behaviour and social media The impact on their wellbeing of a student’s online behaviour and social media use will depend on their skill with and knowledge of the various platforms, their engagement with the online environment and their personal resilience. By learning about students’ online behaviour patterns, and teaching them about the benefits of social media as well as the risks associated with disclosing personal information in online environments, teachers will be better equipped to support their students’ wellbeing. Use of a wellbeing- and strengths-based approach when talking about social media and online behaviour positions teachers to support their students in the ever-changing online and social media environment. Information provided in this package ensures that teachers will have the background knowledge to confidently deliver ReachOut Schools’ social media classroom activities . Use these resources to learn more about concepts relating to students’ online behaviour and social media use. Students and social media Learn more about social media, and about teaching students how to use it safely. Benefits of internet and social media Literature shows that there are many benefits of internet and social media in young peoples lives. How young people seek help online Young people instinctively seek help online rather than seeing face to face services. Disclosing personal information Worried about young people disclosing personal information online? This resource provides tips for support workers on good information security practices. Cyberbullying and students Learn how to support students who have been cyberbullied. Free teaching resources emailed to you Subscribe to our newsletter for the latest teaching resources on mental health and wellbeing. To see how we use this information check out our Privacy policy. Cyberbullying: What is it and how can you stop it? Explore the latest psychological science about the impact of cyberbullying and what to do if you or your child is a victim Mental Health Social Media and Internet Cyberbullying can happen anywhere with an internet connection. While traditional, in-person bullying is still more common , data from the Cyberbullying Research Center suggest about 1 in every 4 teens has experienced cyberbullying, and about 1 in 6 has been a perpetrator. About 1 in 5 tweens, or kids ages 9 to 12, has been involved in cyberbullying (PDF, 5.57MB) . As technology advances, so do opportunities to connect with people—but unfettered access to others isn’t always a good thing, especially for youth. Research has long linked more screen time with lower psychological well-being , including higher rates of anxiety and depression. The risk of harm is higher when kids and teens are victimized by cyberbullying. Here’s what you need to know about cyberbullying, and psychology’s role in stopping it. What is cyberbullying? Cyberbullying occurs when someone uses technology to demean, inflict harm, or cause pain to another person. It is “willful and repeated harm inflicted through the use of computers, cell phones, and other electronic devices.” Perpetrators bully victims in any online setting, including social media, video or computer games, discussion boards, or text messaging on mobile devices. Virtual bullying can affect anyone, regardless of age. However, the term “cyberbullying” usually refers to online bullying among children and teenagers. It may involve name calling, threats, sharing private or embarrassing photos, or excluding others. One bully can harass another person online or several bullies can gang up on an individual. While a stranger can incite cyberbullying, it more frequently occurs among kids or teens who know each other from school or other social settings. Research suggests bullying often happens both at school and online . Online harassment between adults can involve different terms, depending on the relationship and context. For example, dating violence, sexual harassment, workplace harassment, and scamming—more common among adults—can all happen on the internet. How can cyberbullying impact the mental health of myself or my child? Any form of bullying can negatively affect the victim’s well-being, both at the time the bullying occurs and in the future. Psychological research suggests being victimized by a cyberbully increases stress and may result in anxiety and depression symptoms . Some studies find anxiety and depression increase the likelihood adolescents will become victims to cyberbullying . Cyberbullying can also cause educational harm , affecting a student’s attendance or academic performance, especially when bullying occurs both online and in school or when a student has to face their online bully in the classroom. Kids and teens may rely on negative coping mechanisms, such as substance use, to deal with the stress of cyberbullying. In extreme cases, kids and teens may struggle with self-harm or suicidal ideation . How can parents talk to their children about cyberbullying? Parents play a crucial role in preventing cyberbullying and associated harms. Be aware of what your kids are doing online, whether you check your child’s device, talk to them about their online behaviors, or install a monitoring program. Set rules about who your child can friend or interact with on social media platforms. For example, tell your child if they wouldn’t invite someone to your house, then they shouldn’t give them access to their social media accounts. Parents should also familiarize themselves with signs of cyberbullying , such as increased device use, anger or anxiety after using a device, or hiding devices when others are nearby. Communicating regularly about cyberbullying is an important component in preventing it from affecting your child’s well-being. Psychologists recommend talking to kids about how to be safe online before they have personal access to the internet. Familiarize your child with the concept of cyberbullying as soon as they can understand it. Develop a game plan to problem solve if it occurs. Cultivating open dialogue about cyberbullying can ensure kids can identify the experience and tell an adult, before it escalates into a more harmful situation. It’s also important to teach kids what to do if someone else is being victimized. For example, encourage your child to tell a teacher or parent if someone they know is experiencing cyberbullying. Keep in mind kids may be hesitant to open up about cyberbullying because they’re afraid they’ll lose access to their devices. Encourage your child to be open with you by reminding them they won’t get in trouble for talking to you about cyberbullying. Clearly explain your goal is to allow them to communicate with their friends safely online. How can I report cyberbullying? How you handle cyberbullying depends on a few factors, such as the type of bullying and your child’s age. You may choose to intervene by helping a younger child problem solve whereas teens may prefer to handle the bullying on their own with a caregiver’s support. In general, it’s a good practice to take screenshots of the cyberbullying incidents as a record, but not to respond to bullies’ messages. Consider blocking cyberbullies to prevent future harassment. Parents should contact the app or website directly about removing bullying-related posts, especially if they reveal private or embarrassing information. Some social media sites suspend perpetrators’ accounts. If the bullying also occurs at school or on a school-owned device, or if the bullying is affecting a child’s school performance, it may be appropriate to speak with your child’s teacher or school personnel. What are the legal ramifications of cyberbullying? In some cases, parents should report cyberbullying to law enforcement. If cyberbullying includes threats to someone’s physical safety, consider contacting your local police department. What’s illegal can vary from state to state. Any illegal behaviors, such as blackmailing someone to send money, hate crimes, stalking, or posting sexual photos of a minor, can have legal repercussions. If you’re not sure about what’s legal and what’s not, check your state’s laws and law enforcement . Are big tech companies responsible for promoting positive digital spaces? In an ideal world, tech companies would prioritize creating safer online environments for young people. Some companies are working toward it already, including partnering with psychologists to better understand how their products affect kids, and how to keep them safe. But going the extra mile isn’t always profitable for technology companies. For now, it’s up to individuals, families, and communities to protect kids’ and teens’ best interest online. What does the research show about psychology’s role in reducing this issue? Many studies show preventative measures can drastically reduce cyberbullying perpetration and victimization . Parents and caregivers, schools, and technology companies play a role in educating kids about media literacy and mental health. Psychologists—thanks to their expertise in child and teen development, communication, relationships, and mental health—can also make important contributions in preventing cyberbullying. Because cybervictimization coincides with anxiety and depression, research suggests mental health clinicians and educators should consider interventions that both address adolescents’ online experiences and support their mental, social, and emotional well-being. Psychologists can also help parents speak to their kids about cyberbullying, along with supporting families affected by it. You can learn more about cyberbullying at these websites: Cyberbullying Research Center StopBullying.gov Nemours Kids Health Acknowledgments APA gratefully acknowledges the following contributors to this publication: Sarah Domoff, PhD, associate professor of psychology at Central Michigan University Dorothy Espelage, PhD, William C. Friday Distinguished Professor of Education at the University of North Carolina Stephanie Fredrick, PhD, NCSP, assistant professor and associate director of the Dr. Jean M. Alberti Center for the Prevention of Bullying Abuse and School Violence at the University at Buffalo, State University of New York Brian TaeHyuk Keum, PhD, assistant professor in the Department of Social Welfare at the UCLA Luskin School of Public Affairs Mitchell J. Prinstein, PhD, chief science officer at APA Susan Swearer, PhD, Willa Cather Professor of School Psychology, University of Nebraska-Lincoln; licensed psychologist Recommended Reading You may also like Numbers, Facts and Trends Shaping Your World Read our research on: Full Topic List Regions & Countries Publications Our Methods Short Reads Tools & Resources Read Our Research On: Online Harassment 2017 5. Other types of negative experiences online Table of Contents 1. Experiencing online harassment 2. Online harassment in focus: Most recent experience 3. Witnessing online harassment 4. The broader context of online harassment Acknowledgments Methodology While the main focus of this report is the prevalence and impact of specific harassing behaviors, the phenomenon of online harassment takes place within a broader context of negative experiences. This chapter documents three other categories of invasive or inappropriate behaviors people might encounter online: nonconsensual sexual imagery, untrue information and “emergent” forms of invasive and targeted abuse. Each of these topics has garnered a great deal of media attention and sparked conversations about the relative trade-offs of privacy and reputation online. For instance, high-profile incidents of nonconsensual sexual imagery have made headlines for featuring private photos of celebrities (or in a recent instance, members of the military ) and testing the legal limits of state and federal law. But this survey finds that it is remarkably common for everyday people to encounter nonconsensual sexual imagery online. Similarly, the spread of untrue information online was widely covered in the 2016 presidential campaign, which introduced both the broad phenomenon of “ fake news ” and striking examples of misinformation, such as “ Pizzagate ,” in which a Washington, D.C., pizza restaurant was falsely accused of harboring a child sex trafficking ring linked to Hillary Clinton. Most incidents of false information online do not command national attention, but many Americans have had problems as a result of untrue statements being posted about them. Finally, some forms of abuse take advantage of digital vulnerabilities. Tactics like hacking or doxing exploit potential security weaknesses to obtain or publish personal information. Trolling and swatting are designed to overwhelm – the former by manipulating someone’s emotions or reactions, and the latter by calling in fake emergencies so authorities will unexpectedly arrive at someone’s house or other location. While many Americans are not aware of these behaviors, they have all been used to escalate abuse online. Roughly three-in-ten Americans – and just over half of young women – have had explicit images sent to them without their consent New forms of digital communications have made it easier to share explicit images in a way that bypasses consent. Two questions in this survey gauge the extent to which Americans encounter this type of nonconsensual content: if they have ever had explicit images sent to them that they did not ask for, or if they ever had explicit images of themselves shared online without their consent. Overall, 31% of Americans say that someone has sent them explicit images that they did not ask for, while 7% say that someone has shared explicit images of them without their permission. Nearly half of younger Americans (45%) say they have received explicit images they did not ask for, although this experience is not uncommon among older adults either: One-in-five Americans ages 65 and older (20%) have also received this type of unwanted content. Overall, men (30%) and women (32%) are about equally likely to have had someone send them explicit images they did not ask for. But young women in particular encounter this behavior at exceptionally high rates. About half (53%) of women ages 18 to 29 have had someone send them explicit content without their consent, significantly higher than the share of similarly aged men (37%), older men (28%) or older women (26%) who have had this experience. In their own words: Explicit imagery online “Men trolling a friend’s sites by sending d**k pics through Messenger. Then when they didn’t reply the guy stalked them for a year online, commenting, sending more and more private and public messages. It was crazy. She had to block him on a number of occasions and at least once he made a new page with a semi-different spelling to his name.” “Particularly with my female friends, there is a large amount of sexual harassment thrown at their online presences. Sometimes it’s lewd comments on a Facebook photo (even though the commenter is publicly visible), but the worst are on sites like Reddit where users are fully anonymous and they find out about my friends’ gender and begin sending harassing messages or sending lewd pictures.” “Someone shared explicit photos of a friend of mine without her consent.” “Revenge porn posted by daughter’s ex-boyfriend.” “My sister’s ex-husband posted revenge porn. As a way to retaliate.” “Someone posted explicit pictures of me without my permission on a ‘revenge girlfriend’ website.” Meanwhile, young adults of both genders are more likely than adults ages 30 and older to have had explicit images of them shared without their consent (12% vs. 5%). These experiences often go hand-in-hand with the other types of harassing behaviors discussed elsewhere in this report. Among those who have experienced severe forms of online harassment (such as physical threats, stalking, sexual harassment or sustained harassment), 63% have received unwanted explicit images, while 21% have had explicit images of themselves shared without their consent. One-in-four Americans have had false information about them posted online Roughly one-quarter of Americans (26%) say that someone has posted something untrue about them online. The most common type of untrue statement involves people’s personal character or reputation: 17% of Americans say that this type of untrue information about them has been posted online. In addition, roughly one-in-ten Americans say that someone posted something untrue about their relationship or sexual history (9%), while others indicate that someone has posted something untrue related to their political views (6%), job performance (4%), religion (3%), medical history (3%), sexual identity (3%) or gender identity (2%). This experience is especially common among younger adults: 46% of 18- to 29-year-olds say someone has posted something about them online that was not true. At the same time, 29% of 30- to 49-year-olds have had this happen to them, as have 17% of those ages 50 to 64 and 9% of Americans 65 and older. In terms of specific types of untrue content, younger adults are more than twice as likely as those ages 30 and older to say someone has posted untrue statements relating to their character or reputation (30% vs. 13%) or their relationship or sexual history (21% vs. 5%). Overall, there are relatively modest gender differences on this issue. Similar shares of men (28%) and women (23%) say someone has posted something untrue about them online, and men and women tend to experience specific types of untrue content at largely comparable levels. However, a notable minority of young men report that others have posted certain types of untrue content about them. For instance, 11% of men ages 18 to 29 say that someone has posted false information online pertaining to their sexual identity (compared with 4% of similarly aged women and 2% of women as a whole). And 7% of these younger men say someone has posted something untrue about their gender identity (compared with 2% among younger women and just 1% of all women). Having something untrue about oneself posted online might not be considered “online harassment” in a strict sense, but there is often a great deal of overlap between this issue and the other types of harassing behavior discussed elsewhere in this report. About half (51%) of those who have experienced any type of online harassment – including 61% of those who have experienced severe forms of harassment – also say that someone has posted untrue things about them online. By contrast, only 8% of those who have not experienced any type of online harassment indicate that someone has posted something about them online that was not true. In their own words: Untrue information “Someone said I lied about my degree. I was a poser. My husband of two months questioned me. Even my mother, who did not actually get to attend my graduation, questioned me. This person weaved a pretty convincing story.” “I had to take back my business that I was selling to a couple and when they finally had to give it up, they spread rumors that I had been in jail for stealing and I was a meth head and selling drugs on the streets. Then they confiscated my Facebook and posted humiliating things that were going on in my business and that I was hiring some very offensive people to work in my school. And it kept up for about 6-8 months. They finally were run out of town … But they stole a credit card from the business and went on a very large spending spree before letting it go!!!!!” “I have seen friends who had a disagreement spread lies and personal stories of one another.” “A friend was the target of an organized campaign to label her a pedophile because they didn’t agree with her politics.” “Libel – someone purposely made false statements about me and my business. Everyone who knows me knows these names were inaccurate. Problem is that people who don’t know me think the business has prejudiced employers/employees. Roughly half of those who have had untrue information about them posted online have attempted to get it corrected or removed; about seven-in-ten were successful, though many found the process difficult People who have had something untrue posted about them online are split over whether or not they tried to get that information taken down. Roughly half (49%) say they have made some effort to get false information about them corrected or removed, while a similar share (47%) say they have not. Certain groups are more likely than others to try to address false information that gets posted about them. Women are more likely than men to attempt to have this sort of information taken down (57% vs. 43%), and younger adults are more active in this regard than are those ages 30 and older (58% vs. 44%). Meanwhile, about half (53%) of those who have encountered online harassment have attempted to take down untrue information about themselves when it is posted online, compared with 37% of those who have not been harassed online in the past. When asked how successful they were in their efforts to remove or correct untrue information about them posted online, a majority (72%) of those who made the attempt say they ultimately succeeded. However, this group is divided between those who found the process of getting the information removed to be easy (39%) and those who found it difficult (33%). The remaining 28% of those who have tried to get untrue information about them removed were ultimately unsuccessful in their efforts. Roughly one-in-ten Americans have experienced mental or emotional stress because something untrue was posted about them online In some cases, finding untrue information about oneself posted online can take a significant toll. Some 9% of Americans (or 35% of those who have had untrue information posted about them online) have experienced mental or emotional stress as a result of this experience, while similar shares have experienced damage to their reputation (8%) or problems with their friends or family (6%). Others have experienced problems with romantic relationships (4%), problems at school (2%) or work (2%), or financial loss (1%) as a result of untrue information about them being posted online. Younger adults – who, as noted above, are especially likely to encounter situations in which someone has posted untrue information about them online – experience many of these consequences at relatively high rates. Some 17% of 18- to 29-year-olds have experienced mental or emotional stress because something untrue about them was posted online (compared with 7% of those 30 and older). And comparatively large shares of young adults say that untrue information about them online has caused damage to their reputation (15%), problems with friends and family (12%), or problems with romantic relationships (11%). Public awareness of some ‘emergent’ forms of online harassment is mixed Beyond the other issues discussed in this report, the survey also asked about four “emergent” behaviors that have played a prominent role in recent public instances of online harassment. These include: Hacking – illegally accessing someone’s accounts to collect personal information or impersonate them Trolling – intentionally provoking or upsetting people online Doxing – posting someone’s personal information online without their consent Swatting – calling in a fake emergency so that the police will be sent to someone’s house Internet users’ awareness of these emergent issues is relatively mixed. The vast majority of internet users (95%) have heard of hacking, with 58% having heard a great deal about it. Similarly, 86% have heard of trolling, with 42% having heard a great deal. Around one-in-five internet users have experienced these behaviors personally (18% each). On the other hand, doxing and swatting are comparatively less-recognized. Some 73% of online adults have heard of doxing – but only 17% have heard a lot about this issue. In total, 3% of online adults report that they have been doxed. Meanwhile, 55% of internet users have heard of swatting, but only 9% have a great deal of familiarity with this issue. As with many of the behaviors discussed in this chapter, these invasive experiences often go hand-in-hand with other forms of online harassment. Those who have been the targets of some form of online harassment are more likely than those with no harassment experiences to have been trolled (36% vs. 4%), hacked (22% vs. 14%) or doxed (6% vs. 1%). 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Shareable quotes from Americans on online harassment American Trends Panel Wave 24 901 E St. NW, Suite 300 Washington, DC 20004 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries Research Topics Email Newsletters ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan, nonadvocacy fact tank that informs the public about the issues, attitudes and trends shaping the world. It does not take policy positions. The Center conducts public opinion polling, demographic research, computational social science research and other data-driven research. Pew Research Center is a subsidiary of The Pew Charitable Trusts , its primary funder. © 2024 Pew Research Center Online gaming and behavior Research Paper To find inspiration for your paper and overcome writer’s block As a source of information (ensure proper referencing) As a template for you assignment Introduction Works cited. Over the last three decades, technology has drastically changed and so has technology-based games. The rapid increase in popularity has been due to development of the internet infrastructure, which can now support online games that can be accessed from any geographical location. As a result, the effects of computer games can be seen in almost all aspects of today’s lifestyle, from politics, music and Hollywood. Technology’s influence on the gaming community has continued to grow at an alarming rate and with its increase in popularity there have been social and psychological problems that have resulted. Research shows that online gaming activities are one of the most sought after form of lifestyle by kids and young adults, a behavior that is carried along into their adult life. Though the games are popular across all genders, the male gender is one that is affected by the games more than the female gender. Research has shown that due to their nature, online games have an impact on the human brain as they spend most of their times indulging in the game rather than indulging in outdoor activities which are more interactive. The addictive nature of the games can be attributed to the nature of the game that results in the player being indulged in mindless amusement, reduced social interaction, sexism and several antisocial behaviors (Wan & Chiou, 185). These antisocial behaviors are acquired in early childhood or during the early stages of the games and develop further as the person advances. In terms of being social, a gamer tends to isolate himself socially in the name of excessive participation in online games. There are several types of games, some which were created for positive and educative purposes such as mental exercises and teaching. Nevertheless, these games aren’t popular as people opt for more indulging games that provide thrill unlike the academic based ones. As a result, research shows that individuals who indulge in these violent thrill online games develop an increase in their emotional arousal, decrease in brain activity in sections of the brain that are responsible for self organization, self-consciousness and concentration. In addition, it has been known that people engage in online gaming activities to escape social pressure or abuse as they find solace in the games as they don’t directly intrude into the character of the players, as a result they is increased dependency on the games as social ills continue to increase all over the world. As a result, the negative psychological effects arising from online games result in aggressive behaviors with others exhibiting aggressive thoughts towards the society. Also research has shown there is direct relationship between depression on males and engaging in online games as they sit alone and play the addictive games (“The Brief Penguin Handbook” 88). However, the negative effects arising out of continued use of online games will not be uniform across the players as each player has different reasons as to why they engage in the activity. Hence, if the conscience of an individual doesn’t counter the difference between reality and the game, the negative effects of the games on such an individual are bound to increase and be more severe than on persons who can easily counter reality and the online game effects. As a result, individuals who fail to differentiate the two worlds show an increase in violent behaviors directly and indirectly related to the online games, for example an increase in school shootings, suicide, drug and alcohol abuse, stress, anxiety and depression. Massively multiplayer online role-playing games (MMORPGs) are known to influence the day to day activities of the players as the players have to reschedule their daily lives to fit the activity into their program. It is wise to note that the behavior is not only manifested in young people but also in older adults whereby they engage in the activity either in the morning, lunch breaks or during and after work. This indicates how intensively engaged the players are with the game since the games give points to players who play more where they advanced to certain levels. These advance levels give a certain player advantages over other players and thus it is the desire for all players to achieve a certain degree of success unlike their counter parts. This is so as the games are created with the intention of making the players get hooked to them and the social aspect rising out of gaining success over other players. The social aspects of MMORPGs have the off-putting impact of causing excessive playing. Excessive playing for some gamers was caused by their perception that the virtual world is a place to pass time. Excessive play was also aided by the online community (Roberts et al, 12). Online gaming has been viewed as an addictive activity to gamers hence posing as a disadvantage than an advantage. Because the addiction caused by the online games and as a result some gamers skip school or work so that they can play; an action whose outcome is one losing his/her job or school focus. This addiction is normally triggered by societal interaction, in-game tasks as well as competition between gamers. Online gaming has several negative psychosocial implications to the gamers. Some of these negative impacts include the loss of real life friends as well as break up of relationships since the online games impair ones social skills due to one spending a lot of time playing the games instead of interacting with his/her friends. Not only does excessive online gaming lead to dissociation, it also causes loss of time for adolescent gamers apparently whom, instead of focusing on more constructive activities such as their studies, they tend to waste their time playing. The main reason for time loss for online gamers is the extent of involvement in excessive playing. Sanders et al notes “Sure video games maybe therapeutic for some people, but the small amount of people who are negatively affected by gaming impact many. So just like one alcoholic can cause a car accident that can kill twenty people, increased promotion of violence coupled with real life graphics can do the same thing.”(78). Studies carried out have resulted to the conclusion that violent online games may cause negative behavioral changes in young adults at their adolescent especially in males. The worst effects of these violent online games are neurological and also behavioral changes in young men. Violent online games ought to interfere with the brain’s ability to control emotions hence lead to aggressive behavior. Conclusively, excessive online gaming results to a long term effect on ones brain and results to changes of one’s behavior on a negative scale since it translates to aggressive behavior on the part of young males. The more time spent in playing violent games online, the more the increase in physical aggression particularly in young males since they are the most probable online players of violent games. The aggression triggered by the violent games leads to the gamers participating in criminal as well as hazardous activities such as substance abuse, alcohol dependence and also gambling. Having an addiction from an excessive habit such as excessive online gaming, one acquires both social as well as emotional impairments. Online gaming has the most negative psychological implications to gamers such as: a decrease in social interactions, increased physical aggression and aggressive behavior and also an increase in physiological stimulation (Grusser et al, 292). It is evident that excessive gaming habits will lead to social and psychological problems for adolescents, especially an increase in aggressive behavior in males. On social terms, when social integration is looked into in terms of investigating the implications of online gaming, the result outcome of excessively indulging in online games is development of depression since excessive gamers lack the necessary comfort offered by real presence and association of people. Due to social problems associated with intensive online gaming, loneliness of the players comes into effect since they become socially handicapped, in event; depression comes into being. Prolonged online gaming may cause neglect of one’s duties and also interference in ones social groups as well as participation in social duties that don’t involve gaming. As for psychological problems brought about by online gaming in young males in their adolescence, depression acts as a psychological end result of excessive online gaming. At the same time, the loneliness that the gamers gradually suffer causes venomous effects on one’s psychological state. Due to loneliness and withdrawal, the player tends to be aggressive to his peers and friends. Another cause of aggression is the psychological interference of the player particularly in event where he excessively participates in playing violent games. This factor greatly increases aggressiveness in the players particularly young adolescent males since they participate in playing violent games more than their female counterparts. The violence portrayed in the games affects the player’s brain in that he lacks the ability to have emotional control and thus result to portraying aggressive behavior. Grusser, Sanchez, Thalemann Ring, and Griffiths Davison. “Excessive computer game playing: evidence for addiction and aggression?” Cyber Psychology & Behavior 10 (2007): 290–292. Print. Roberts, Tammy, et al. The Broadview Anthology of Expository Prose. 2 nd edition. Peterborough, Ont: Broadview, 2011. Print. Sanders, Edwin, et al. “The relationship of internet use to depression and social isolation among adolescents.” Adolescence 35 (2000): 237-242. Print. The Brief Penguin Handbook . 2 nd Canadian Edition, with MyCanadianCompLab. Toronto: Pearson, 2009. Print. Wan, Cheng, and Chiou Wui. “The motivations of adolescents who are addicted to online games: a cognitive perspective.” Adolescence 42 (2007): 181–197. Print. Societal Structural Order and Change Can’t Stop Won’t Stop: A History of the Hip-Hop Generation The Nature Versus Nurture Debate: Sociology Experience Gaming Industry: Budget, Quality, and Reception Living Through Enjoyment: Live-Action Role-Play Homosexuality Criticism Rates Stereotyping in the Human Culture Japanese Sociolinguistics: Identity and Power in the Workplace or Family Social Work Organizations Globalization and Internet Chicago (A-D) Chicago (N-B) IvyPanda. (2019, April 2). Online gaming and behavior. https://ivypanda.com/essays/online-gaming-and-behavior/ "Online gaming and behavior." IvyPanda , 2 Apr. 2019, ivypanda.com/essays/online-gaming-and-behavior/. IvyPanda . (2019) 'Online gaming and behavior'. 2 April. IvyPanda . 2019. "Online gaming and behavior." April 2, 2019. https://ivypanda.com/essays/online-gaming-and-behavior/. 1. IvyPanda . "Online gaming and behavior." April 2, 2019. https://ivypanda.com/essays/online-gaming-and-behavior/. Bibliography IvyPanda . 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Turning off personalized advertising allows you to exercise your right to opt out. Learn more in IvyPanda's Cookies Policy and Privacy Policy . Essay on Human Behaviour Students are often asked to write an essay on Human Behaviour in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic. Let’s take a look… 100 Words Essay on Human Behaviour Understanding human behavior. Human behavior is the way people act and react. It can be influenced by many things like feelings, the environment, or past experiences. For example, someone might be very happy at a party but sad if they lose a game. Influences on Behavior What we do is often shaped by where we are and who we are with. If we are with friends, we might act differently than when we are alone. Also, rules at home or school guide how we behave. Learning from Others We learn how to act by watching others. If a child sees a sibling sharing toys, they may learn to share too. This is called learning by example. It’s a big part of growing up. Choices and Behavior Every day, we make choices that show our behavior. Choosing to help someone or saying ‘thank you’ are ways we can show kindness. Our choices help shape our behavior. Changing Behavior 250 words essay on human behaviour. Human behavior is the way people act and think. It’s what we do every day, like talking, playing, and even sleeping. Scientists who study how the mind works say that many things shape our actions. These include how we grow up, the friends we have, and the rules we follow. Why We Act Differently Each person is unique, which means we don’t all behave the same way. Imagine you have a twin. Even if you look the same, you might like different foods or games. This is because our choices often depend on our own personal experiences and feelings. We learn a lot by watching other people. For example, if you see someone getting a reward for being kind, you might start being kinder too. This is called learning by example. Parents, teachers, and friends can all be examples that we learn from. Feelings and Behavior Our feelings play a big part in how we act. If you are happy, you might smile or laugh. But if you’re sad, you might not want to play. Understanding our emotions can help us know why we act in certain ways. Choices and Consequences Every action has a result, which can be good or bad. If you study hard, you might get good grades. This shows that making good choices can lead to good things happening. 500 Words Essay on Human Behaviour Human behavior is the way people act and think in different situations. It’s like a code that tells us what someone might do next. Everyone is unique, so their actions can be very different from one another. Scientists and psychologists study human behavior to learn why we do the things we do. Why We Act the Way We Do A big part of our behavior comes from our brains. Our thoughts and feelings can push us to act in certain ways. For example, if you feel happy, you might smile or laugh. If you are scared, you might run away or hide. Our families, friends, and even the places we live also shape our actions. They teach us what is okay to do and what is not. Choices and Decisions Every day, we make choices. Some are small, like what to wear, and some are big, like whom to be friends with. When we make a choice, we think about the good and bad things that could happen. This thinking helps us decide what to do. Our choices can affect not just us, but also other people around us. Feelings play a huge role in our actions. When we are happy, we may do kind things. When we are angry, we might do things we later wish we hadn’t. Understanding our feelings can help us control our actions better. It’s like having a remote control for our behavior. Group Behavior Change over time. As we get older, our behavior changes. A little child might throw toys when angry, but as they grow, they learn new ways to show they are upset. We all learn and change as we have more experiences. It’s like getting updates on a computer; we get better at handling different situations. Human behavior is a big and interesting subject. It’s about why we do things, how we learn from others, and how we change over time. By understanding behavior, we can get along better with others and make good choices. It’s important for us to think about why we act the way we do, so we can be the best versions of ourselves. That’s it! I hope the essay helped you. Apart from these, you can look at all the essays by clicking here . Happy studying! Leave a Reply Cancel reply 374 IMAGES My Behavior Essay (400 Words) Social Behavior Essay Example Behavior Essay by Clarinda Copeland ≫ Online Shopping Behavior Free Essay Sample on Samploon.com Behavior Essay For Students To Copy Teaching Resources Appropriate Classroom Behavior Essay Elegant Sample Student Classroom VIDEO Cyberbully Experiment Series Compilation The Case Formulation Approach to Cognitive-Behavior Therapy How does Online Behavioural Advertising works? Effective Communication with Law Enforcement Online study advantages and disadvantages Punjabi Essay |ਆਨਲਾਈਨ ਪੜਾਈ ਦੇ ਲਾਭ ਅਤੇ ਹਾਨੀਆਂ ਪੰਜਾਬੀ ਲੇਖ Psychoanalysis and Sigmund Freud’s Contribution to It COMMENTS How Can We Better Understand Online Behavior? Northeastern scientists will recruit a small sample of people (roughly 2,000) for a rigorous examination of their online behavior, including how and how often they use major platforms. Then, they'll recruit a larger sample (tens of thousands of people) to assess broader population trends, using the more granular information from the smaller ... The "online brain": how the Internet may be changing our cognition How does the Internet gain and sustain our attention? The Internet consumes a considerable chunk of our attention on a day‐to‐day basis. The vast majority of adults go online daily, and over a quarter report being online "almost constantly"2.Within this, one in five American adults are now "smartphone‐only" Internet users1.Importantly, the introduction of these Internet‐enabled ... The Social Net: Understanding our online behavior Abstract. In 2005, The Social Net was a pioneering project to bring together contributions from leading scholars on the major topics pertaining to the social aspects of the online world. The book has been a great success and has helped many, including students, academics, and lay people, to attain a broad comprehensive knowledge of online ... Online Behavior of Students Online Behavior of Students Essay. Online behavior of students is an interesting topic for discussion because it still has not been researched well enough. The Internet allows students to express their opinions in various, previously unavailable ways. Online interactions may lead to new relationships and sometimes have a positive influence on ... Two social lives: How differences between online and offline For hundreds of thousands of years, humans have primarily communicated and connected in person. But social life has dramatically changed in recent decades—starting when the first email was sent in 1965—shifting from fewer offline (in-person) interactions to more online (technology-mediated) interactions. Now, one out of every four American adults report being online 'almost constantly ... Essays on user behavior in online platforms The second essay studies the dynamics of students' online social engagement in the context of online learning and how the recent COVID-19 pandemic affected it. I propose a Hidden Markov Model (HMM) with students' self-efficacy as hidden states to investigate the dynamics of elementary school students' online social behavior. The Netiquette and Its Significance in Online Behavior Great habits, one aspect of refined talent, are frequently linked to behavior. The term "netiquette" refers to established guidelines for professional and courteous online behavior. Online correspondence has certain provisions that make it more vulnerable to netiquette violations. 6 Explores the ways we can regulate online behavior. Larry Lessig divides regulation into laws, social norms, markets, and technology. As we'll see, ideas about "free speech" vary around the world, and laws about hate speech are quite different in the United States compared to other nations. Where we draw the line between free speech and ... Understanding Online Behavior: Exploring the Probability of Online Introduction. Human-computer interaction (HCI) is a convolutional discipline that integrates diverse research disciplines. Research on HCI involves the study of broader societal implications and interactions that are based on computer system usage (Hooper and Dix, 2013).The integration of human psychological studies in HCI studies for understanding human dynamics on the Internet has received ... Essay about Social Media and Behavioral Effects Using online social networks such as Facebook and Twitter enable users to establish groups comprised of people with similar interests, values, and beliefs. These include personal stories, many different forms of entertainment, work- and school-related information, social events, and a wide variety of other social functions (Cheung et al., 2011 ... PDF The Effect of Digital Citizenship on Negative Online Behaviors and citizenship behavior and negative online behavior and learning outcomes. Several studies have proposed that digital citizenship is important in higher education settings (Al-Zahrani, 2015; Kim & Choi, 2018; Pedersen et al., 2018); however, research on this phenomenon is scant. Thus, the purpose of this study is Problematic Online Behaviors among Adolescents and Emerging Adults Over the past two decades, young people's engagement in online activities has grown markedly. The aim of the present study was to examine the relationship between two specific online behaviors (i.e., cyberbullying perpetration, problematic social media use) and their relationships with social connectedness, belongingness, depression, and self-esteem among high school and university students ... Netiquette: The do's & don'ts of online behaviour When you're studying online, your contact with peers and facilitators will be through web-based forums and discussion boards. It's worth making sure that you're coming across the way you intend to and that you're a positive addition to your online community. Here are a few Dos and Don'ts of "netiquette" — or online communication ... Behind Online Behavior Liraz Margalit Ph.D. on May 9, 2020. Neuroarchitecture is a field of research that examines how to design spaces and interfaces that affect the mental, physical, and emotional health of the people ... Can we better understand online behavior? These researchers will dig In the 21st century, our lives are online: Around the world, we can shop, socialize, bank, attend events, visit doctors, watch TV, listen to music, order takeout, work, learn, and much more, all with an internet connection. However, a vast majority of these activities are facilitated by a handful of digital gatekeepers, leaving everyone else […] Online Social Networking and Mental Health Abstract. During the past decade, online social networking has caused profound changes in the way people communicate and interact. It is unclear, however, whether some of these changes may affect certain normal aspects of human behavior and cause psychiatric disorders. Several studies have indicated that the prolonged use of social networking ... The Online Behaviour Taxonomy: A conceptual framework to understand Arguably, whether the source of behaviour itself originates online (online-exclusive) or is mediated by internet-enabled devices/platforms (e.g., online-mediated) is a critical factor in understanding the psychological and social effects associated with CMC, ranging from motivation to behavioural outcomes. Digital Literacy and Online Ethics Grade Level: 8-12. Student learning objectives. Students will gain digital literacy (media literacy, information literacy) skills by considering the human and legal consequences of poor choices others have made online. Students will recognize that even when online expression is protected by the First Amendment, it still can result in legal ... Online behaviour and social media By learning about students' online behaviour patterns, and teaching them about the benefits of social media as well as the risks associated with disclosing personal information in online environments, teachers will be better equipped to support their students' wellbeing. Use of a wellbeing- and strengths-based approach when talking about ... Cyberbullying: What is it and how can you stop it? Cyberbullying occurs when someone uses technology to demean, inflict harm, or cause pain to another person. It is "willful and repeated harm inflicted through the use of computers, cell phones, and other electronic devices.". Perpetrators bully victims in any online setting, including social media, video or computer games, discussion boards ... Other types of negative experiences online 5. Other types of negative experiences online. While the main focus of this report is the prevalence and impact of specific harassing behaviors, the phenomenon of online harassment takes place within a broader context of negative experiences. This chapter documents three other categories of invasive or inappropriate behaviors people might ... Online gaming and behavior Research shows that online gaming activities are one of the most sought after form of lifestyle by kids and young adults, a behavior that is carried along into their adult life. Though the games are popular across all genders, the male gender is one that is affected by the games more than the female gender. Research has shown that due to their ... Essay on Human Behaviour 100 Words Essay on Human Behaviour Understanding Human Behavior. Human behavior is the way people act and react. It can be influenced by many things like feelings, the environment, or past experiences. For example, someone might be very happy at a party but sad if they lose a game. assignment essay homework paper phd report resume review speech thesis