  With the rise of social media, people can now form relationships and
communities easily regardless of location, race, ethnicity, or gender. However,
the power of social media simultaneously enables harmful online behavior such
as harassment and bullying. Cyberbullying is a serious social problem, making
it an important topic in social network analysis. Machine learning methods can
potentially help provide better understanding of this phenomenon, but they must
address several key challenges: the rapidly changing vocabulary involved in
cyber- bullying, the role of social network structure, and the scale of the
data. In this study, we propose a model that simultaneously discovers
instigators and victims of bullying as well as new bullying vocabulary by
starting with a corpus of social interactions and a seed dictionary of bullying
indicators. We formulate an objective function based on participant-vocabulary
consistency. We evaluate this approach on Twitter and Ask.fm data sets and show
that the proposed method can detect new bullying vocabulary as well as victims
and bullies.
