  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 and/or 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.
  In this work, we take the first concrete steps to understand the
characteristics of abusive behavior in Twitter, one of today's 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 hate-related, 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 hate-related communities (Gamergate). We present a robust
methodology to distinguish bullies and aggressors from normal Twitter users by
considering text, user, and network-based attributes. Using various
state-of-the-art 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.
