In the Service of Online Order: Tackling Cyber-Bullying with Machine Learning and Affect Analysis

• URL: http://arxiv.org/abs/2203.02116v1
• Date: Fri, 4 Mar 2022 03:13:45 GMT
• Title: In the Service of Online Order: Tackling Cyber-Bullying with Machine Learning and Affect Analysis
• Authors: Michal Ptaszynski, Pawel Dybala, Tatsuaki Matsuba, Fumito Masui, Rafal Rzepka, Kenji Araki, Yoshio Momouchi
• Abstract summary: 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. We aim to develop a set of tools that can automatically detect malicious entries and report them to PTA members.
• Score: 13.092135222168324
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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