Cyberbullying perpetration and victimization among children and adolescents: A systematic review of longitudinal studies Abstract   In this systematic review of exclusively longitudinal studies on cyberbullying perpetration and victimization among adolescents, we identified 76 original longitudinal studies published between 2007 and 2017. The majority of them approached middle school students in two waves at 6 or 12 months apart. Prevalence rates for cyberbullying perpetration varied between 5.3 and 66.2 percent, and for cyberbullying victimization between 1.9 and 84.0 percent. Personrelated factors (e.g., traditional bullying, internalizing problems) were among the most studied concepts, primarily examined as significant risk factors. Evidence on the causal relationships with mediarelated factors (e.g., (problematic) Internet use), and environmental factors (e.g., parent and peer relations) was scarce. This review identified gaps for future longitudinal research on cyberbullying perpetration and victimization in childhood and adolescence.

Detecting Cyberbullying and Cyberaggression in Social Media Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged andor coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts.  :66,this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of todayu0027s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be haterelated, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the haterelated communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and networkbased attributes. Using various stateoftheart machine learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology, and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future.