Abstract
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 non-bullying 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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