Cyber Bullying, which often has a deeply negative impact on the victim, has grown as a serious issue among adolescents. To understand the phenomenon of cyber bullying, experts in social science have focused on personality, social relationships and psychological factors involving both the bully and the victim. Recently computer science researchers have also come up with automated methods to identify cyber bullying messages by identifying bullying-related keywords in cyber conversations. However, the accuracy of these textual feature based methods remains limited. In this work, we investigate whether analyzing social network features can improve the accuracy of cyber bullying detection. By analyzing the social network structure between users and deriving features such as number of friends, network embeddedness, and relationship centrality, we find that the detection of cyber bullying can be significantly improved by integrating the textual features with social network features.
This exploratory work studies the effects of emerging app features on the cyberbullying practices in high school settings. These include the increasing prevalence of image/video content, perceived ephemerality, anonymity, and hyperlocal communication. Based on qualitative analysis of focus groups and follow-up individual interviews with high school students, these features were found to influence the practice of cyberbullying, as well as creating negative socio-psychological effects. For example, visual data was found to be used in cyberbullying settings as evidence of contentious events, a repeated reminder, and caused a graphic impact on recipients. Similarly, perceived ephemerality of content was found to be associated with "broken expectations" with respect to the apps and severe bullying outcomes for those affected. Results shed light on an important technology-mediated social phenomenon of cyberbullying, improve understanding of app use (and abuse) by the teenage user population, and pave the way for future research on countering app-centric cyberbullying.
Cyberbullying is an important social challenge that takes place over a technical substrate. Thus, it has attracted research interest across both computational and social science research communities. While the social science studies conducted via careful participant selection have shown the effect of personality, social relationships, and psychological factors on cyberbullying, they are often limited in scale due to manual survey or ethnographic study components. Computational approaches on the other hand have defined multiple automated approaches for detecting cyberbullying at scale, and have largely focused only on the textual content of the messages exchanged. There are no existing efforts aimed at testing, validating, and potentially refining the findings from traditional bullying literature as obtained via surveys and ethnographic studies at scale over online environments. By analyzing the social relationship graph between users in an online social network and deriving features such as out-degree centrality and the number of common friends, we find that multiple social characteristics are statistically different between the cyberbullying and nonbullying groups, thus supporting many, but not all, of the results found in previous survey-based bullying studies. The results pave way for better understanding of the cyberbullying phenomena at scale.
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