论文标题
在在线订单的服务中：通过机器学习和影响分析来解决网络欺凌
In the Service of Online Order: Tackling Cyber-Bullying with Machine Learning and Affect Analysis
论文作者
论文摘要
最近，日本最近的燃烧问题之一是网络欺凌或在线欺骗和欺负人们。在日本学校的非正式网站上特别注意了这个问题。由学校人员和PTA（家长教师协会）成员组成的志愿者已经开始在线巡逻，以在网络论坛和博客中发现恶意内容。实际上，在线巡逻队假设阅读整个网络内容，这是一项难以手动执行的任务。在本文中，我们介绍了一项研究，旨在帮助PTA成员更有效地在线巡逻。我们旨在开发一套可以自动检测恶意条目并将其报告给PTA成员的工具。首先，我们从非正式学校网站收集了网络欺凌数据。然后，我们通过两种方式对这些数据进行了分析。首先，我们通过多方面的情感分析系统分析了条目，以便找到网络欺凌的独特功能，并将其应用于机器学习分类器。其次，我们应用了基于SVM的机器学习方法来训练分类器以检测网络欺凌。该系统能够以平衡的F-评分为88.2％的网络欺凌条目进行分类。
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.
