  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 incidents of cyberbullying
over images in Instagram, a media-based mobile social network. To this end, we
have collected a sample Instagram data set consisting of images and their
associated comments, and designed a labeling study for cyberbullying as well as
image content using human labelers at the crowd-sourced Crowdflower Web site.
An analysis of the labeled data is then presented, including a study of
correlations between different features and cyberbullying as well as
cyberaggression. Using the labeled data, we further design and evaluate the
accuracy of a classifier to automatically detect incidents of cyberbullying.
