@article{394,
  author = {Michal Ptaszynski , Pawel Dybala , Tatsuaki Matsuba , Fumito Masui , Rafal Rzepka , Kenji Araki , Yoshio Momouchi },
  title = {In the Service of Online Order: Tackling Cyber-Bullying with Machine Learning and Affect Analysis},
  journal = {International Journal of Computational Linguistics Research},
  year = {2010},
  volume = {1},
  number = {3},
  doi = {},
  url = {http://www.dline.info/jcl/fulltext/v1n3/3.pdf},
  abstract = {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.},
}