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

TL;DR

This paper presents a machine learning approach using affect analysis and SVM to automatically detect cyber-bullying on Japanese school websites, aiming to assist volunteers in monitoring online content more efficiently.

Contribution

It introduces a novel system combining affect analysis and SVM classification to identify cyber-bullying, improving detection accuracy and aiding online patrol efforts.

Findings

Achieved 88.2% balanced F-score in classifying cyber-bullying.

Developed a set of tools to assist PTA members in monitoring online content.

Analyzed cyber-bullying data with affect features for effective machine learning application.

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.