Predicting cyberbullying from anonymity.

Barlett, Christopher P.; Gentile, Douglas A.; Chew, Chelsea

doi:10.1037/ppm0000055

Cited by 139 publications

(101 citation statements)

References 26 publications

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“…Although researchers have acknowledge that theories and studies of traditional bullying are not applicable to cyberbullying due to differences between online and offline contexts Slonje & Smith 2007), little research has examined exactly what makes the context of cyberbullying different from that of traditional bullying. Four studies that examined this issue highlighted the role of anonymity (Barlett et al 2014;. Although this is an insightful start, these studies did not present anonymity as it is understood in the theoretical information systems (IS) literature and thus did not explain how and why it encourages cyberbullying or what creates anonymity itself.…”

Section: Introductionmentioning

confidence: 99%

Why Do Adults Engage in Cyberbullying on Social Media? An Integration of Online Disinhibition and Deindividuation Effects with the Social Structure and Social Learning Model

Lowry

Zhang

Wang

et al. 2016

Information Systems Research

273

197

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Section: Introductionmentioning

confidence: 99%

Why Do Adults Engage in Cyberbullying on Social Media? An Integration of Online Disinhibition and Deindividuation Effects with the Social Structure and Social Learning Model

Lowry

Zhang

Wang

et al. 2016

Information Systems Research

273

197

View full text Add to dashboard Cite

show abstract

“…IRB approval was granted by the corresponding author's ethics committee. Participants were solicited via a posting in the college's Student Digest that purportedly went to the entire college student body (similar methods were used by Barlett et al, ) and interested participants completed the online informed consent before the aforementioned questionnaires at Wave 1 (dates of data collection were 9‐15‐14 to 9‐19‐14), Wave 2 (dates of data collection were 1‐7‐15 to 1‐16‐15), and Wave 3 (dates of data collection were 3‐31‐15 to 4‐7‐15). After data collection was complete, participants were compensated, thanked, and fully debriefed.…”

Section: Methodsmentioning

confidence: 99%

“…The BGCM posits that individuals are more likely to feel anonymous in IM programs because aggressors can replace their real names and identity with fake names (or handles)—a characteristic of IM that is absent in email and text messaging (mostly). Indeed, Barlett, Gentile, and Chew () found that IM frequency was positively related to the perception that harm can be done online anonymously (anonymity perceptions), which predicted positive attitudes towards cyberbullying and subsequent cyberbullying. In contrast e‐mail was negatively related to these anonymity perceptions.…”

Section: Anonymity Perceptions and Belief In The Irrelevance Of Physimentioning

confidence: 99%

“…Indeed, we are unaware of any longitudinal study that has tested the full BGCM. Several studies have longitudinally shown that anonymity perceptions (Barlett, ; Barlett et al, ) or the belief that muscularity is irrelevant online (Barlett et al, in press) predicts cyberbullying through cyberbullying attitudes. Only correlational research (Barlett & Gentile, ) has tested the combined influence of both anonymity perceptions and BI‐MOB in the same model.…”

Section: Expanding Our Theoretical Understanding Of Cyberbullyingmentioning

confidence: 99%

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Predicting cyberbullying perpetration in emerging adults: A theoretical test of the Barlett Gentile Cyberbullying Model

Barlett

Chamberlin

Witkower

2016

Aggressive Behavior

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The Barlett and Gentile Cyberbullying Model (BGCM) is a learning-based theory that posits the importance of positive cyberbullying attitudes predicting subsequent cyberbullying perpetration. Furthermore, the tenants of the BGCM state that cyberbullying attitude are likely to form when the online aggressor believes that the online environment allows individuals of all physical sizes to harm others and they are perceived as anonymous. Past work has tested parts of the BGCM; no study has used longitudinal methods to examine this model fully. The current study (N = 161) employed a three-wave longitudinal design to test the BGCM. Participants (age range: 18-24) completed measures of the belief that physical strength is irrelevant online and anonymity perceptions at Wave 1, cyberbullying attitudes at Wave 2, and cyberbullying perpetration at Wave 3. Results showed strong support for the BGCM: anonymity perceptions and the belief that physical attributes are irrelevant online at Wave 1 predicted Wave 2 cyberbullying attitudes, which predicted subsequent Wave 3 cyberbullying perpetration. These results support the BGCM and are the first to show empirical support for this model. Aggr. Behav. 43:147-154, 2017. © 2016 Wiley Periodicals, Inc.

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“…This is accompanied by empirical support. For example, in a longitudinal study on 146 college students over a year, Barlett, Gentile, & Chew (2016) found that especially among those who have an inclination towards bulling others, greater initial feelings of anonymity online 41 are linked to higher incidences of reported "cyberbullying" over time.…”

Section: Figure 26: Implicit Associations Between Party and Between mentioning

confidence: 99%

Internet Use and Political Polarization: A Review

Deri¹

2019

Preprint

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In this paper, I attempt to provide a comprehensive review of the evidence regarding the relationship between political polarization in the US and internet use. In the first part, I examine whether there has indeed been a rise in political polarization in the US in the last several decades. The remaining second and third parts deal with the relationship between polarization and internet use. I begin, in the second part, by reviewing evidence pertaining to the question of whether internet use plays a causal role in bringing about polarization. I then move, in the third part, to exploring the possible means by which internet use might bring about polarization. By analogy to cigarettes and cancer, the second part examines whether cigarette smoking causes cancer, while the third part examines how cigarette smoking causes (or might cause) cancer. One focus, in the third section, is on the most often discussed mechanism of internet-caused polarization: segregated information exposure, which corresponds to claims that polarization is driven by an internet ecosystem characterized by “echo chambers”, “filter bubbles”, and otherwise partisan information consumption and dissemination.The brief summary for each of the three parts is this. First, there is evidence that polarization has been on the rise in the U.S. in the recent decades—but it depends what you measure. When comparing Republican and Democrats, there is strongest evidence for increases in affective polarization and policy-based polarization. Second, most analyses would marshal against a version of reality where the rise in the political polarization in recent decades is mostly accounted for by the dramatic rise in internet use over this same time period. However, one notable, well-conducted, large-scale randomized direct intervention study confirms that de-activating a social media account (Facebook) resulted in significant and non-trivially sized decreases in polarization, specifically related to political opinions and policy preferences (Allcott, Braghieri, Eichmeyer, & Gentzkow, 2019). Finally, the evidence is murkiest regarding how internet use might drive polarization. With regard to polarization via segregated information exposure, claims that partisans inhabit wildly segregated “echo chambers” or “filter bubbles” are largely overstated. Nevertheless, there are significant and meaningful differences in the political content that partisans of different political orientations consume online, comparable to the degree of segregation in national print newspaper readership. Causal evidence linking this differential exposure to political polarization is not as strong as evidence that differential exposure exists. Evidence for other mechanisms of polarization is suggestive but awaits strong empirical confirmation.

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Predicting cyberbullying from anonymity.

Cited by 139 publications

References 26 publications

Why Do Adults Engage in Cyberbullying on Social Media? An Integration of Online Disinhibition and Deindividuation Effects with the Social Structure and Social Learning Model

Why Do Adults Engage in Cyberbullying on Social Media? An Integration of Online Disinhibition and Deindividuation Effects with the Social Structure and Social Learning Model

Predicting cyberbullying perpetration in emerging adults: A theoretical test of the Barlett Gentile Cyberbullying Model

Internet Use and Political Polarization: A Review

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