  In recent years, bullying and aggression against users on social media have
grown significantly, causing serious consequences to victims of all
demographics. In particular, cyberbullying affects more than half of young
social media users worldwide, and has also led to teenage suicides, prompted by
prolonged and/or coordinated digital harassment. Nonetheless, tools and
technologies for understanding and mitigating it are scarce and mostly
ineffective. In this paper, we present a principled and scalable approach to
detect bullying and aggressive behavior on Twitter. We propose a robust
methodology for extracting text, user, and network-based attributes, studying
the properties of cyberbullies and aggressors, and what features distinguish
them from regular users. We find that bully users post less, participate in
fewer online communities, and are less popular than normal users, while
aggressors are quite popular and tend to include more negativity in their
posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3
months, and show that machine learning classification algorithms can accurately
detect users exhibiting bullying and aggressive behavior, achieving over 90%
AUC.
