  One of the burning problems lately in Japan has been cyber-bullying, or
slandering and bullying people online. The problem has been especially noticed
on unofficial Web sites of Japanese schools. Volunteers consisting of school
personnel and PTA (Parent-Teacher Association) members have started Online
Patrol to spot malicious contents within Web forums and blogs. In practise,
Online Patrol assumes reading through the whole Web contents, which is a task
difficult to perform manually. With this paper we introduce a research intended
to help PTA members perform Online Patrol more efficiently. We aim to develop a
set of tools that can automatically detect malicious entries and report them to
PTA members. First, we collected cyber-bullying data from unofficial school Web
sites. Then we performed analysis of this data in two ways. Firstly, we
analysed the entries with a multifaceted affect analysis system in order to
find distinctive features for cyber-bullying and apply them to a machine
learning classifier. Secondly, we applied a SVM based machine learning method
to train a classifier for detection of cyber-bullying. The system was able to
classify cyber-bullying entries with 88.2% of balanced F-score.
