  Cyberbullying is a growing problem affecting more than half of all American
teens. The main goal of this paper is to investigate fundamentally new
approaches to understand and automatically detect and predict incidents of
cyberbullying in Instagram, a media-based mobile social network. In this work,
we have collected a sample data set consisting of Instagram images and their
associated comments. We then designed a labeling study and employed human
contributors at the crowd-sourced CrowdFlower website to label these media
sessions for cyberbullying. A detailed analysis of the labeled data is then
presented, including a study of relationships between cyberbullying and a host
of features such as cyberaggression, profanity, social graph features, temporal
commenting behavior, linguistic content, and image content. Using the labeled
data, we further design and evaluate the performance of classifiers to
automatically detect and pre- dict incidents of cyberbullying and
cyberaggression.
