  While social media offer great communication opportunities, they also
increase the vulnerability of young people to threatening situations online.
Recent studies report that cyberbullying constitutes a growing problem among
youngsters. Successful prevention depends on the adequate detection of
potentially harmful messages and the information overload on the Web requires
intelligent systems to identify potential risks automatically. The focus of
this paper is on automatic cyberbullying detection in social media text by
modelling posts written by bullies, victims, and bystanders of online bullying.
We describe the collection and fine-grained annotation of a training corpus for
English and Dutch and perform a series of binary classification experiments to
determine the feasibility of automatic cyberbullying detection. We make use of
linear support vector machines exploiting a rich feature set and investigate
which information sources contribute the most for this particular task.
Experiments on a holdout test set reveal promising results for the detection of
cyberbullying-related posts. After optimisation of the hyperparameters, the
classifier yields an F1-score of 64% and 61% for English and Dutch
respectively, and considerably outperforms baseline systems based on keywords
and word unigrams.
