Automatic Cyberbullying Detection on Twitter Using Bullying Expression Dictionary.

Cyberbullying has become a serious problem with the spread of personal computers, smartphones and SNS. In this paper, for automatic cyberbullying detection on Twitter, we construct a bullying expression dictionary, which registers bullying words and their degrees related to bullying. The words registered in the dictionary are those that appear in the collected bullying-related tweets, and the bullying degrees attached to the words are calculated using SO-PMI. We also construct models to automatically classify bullying and non-bullying tweets by extracting multiple features including the bullying expression dictionary and combining them with multiple machine learning algorithms. We evaluate the classification performance of bullying and non-bullying tweets using the constructed models. The experimental results show that the bullying expression dictionary can contribute to cyberbullying detection in most of the machine learning algorithms and that the best model can obtain an evaluation of over 90%.