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.

该研究旨在帮助家长教师会成员更有效地对网络论坛和博客的恶意内容进行监测，以实际应用机器学习等技术进行网络欺凌入口的分类和识别。通过对不同学校的网络平台数据进行的研究，该研究成功训练了机器学习分类器，最终分类准确率达到 88.2% 的平衡 F 分数。