Detecting cyberbullying: query terms and techniques
Detecting cyberbullying: query terms and techniques
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DOI：
10.1145/2464464.2464499
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发表时间：
2013-05
期刊：
影响因子：
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通讯作者：
April Kontostathis;Kelly Reynolds;Andy Garron;Lynne Edwards
中科院分区：
文献类型：
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作者：
April Kontostathis;Kelly Reynolds;Andy Garron;Lynne Edwards
在本文中，我们对网络欺凌中使用的语言进行了仔细分析。我们将 Formspring.me 的帖子集合作为我们的语料库。 Formspring.me 是一个社交网站，用户可以在其中向其他用户提问。它主要吸引青少年和年轻人，并且网站上的网络欺凌内容非常密集；我们分析的帖子中有 7% 到 14% 包含网络欺凌内容。本文提出的结果有两个方面。我们的第一个实验旨在加深对网络欺凌者使用的特定单词以及这些单词周围的上下文的理解。我们已经确定了最常用的网络欺凌术语，并开发了可用于检测网络欺凌内容的查询。我们的五个查询在排名 100 时达到了 91.25% 的平均精度。在我们的第二组实验中，我们通过使用监督机器学习方法来检测网络欺凌来扩展这项工作。机器学习实验确定了与网络欺凌内容一致的其他术语，并确定了一种额外的查询技术，该技术能够准确地为 Formspring.me 的帖子分配分数。得分最高的帖子显示出网络欺凌内容的密度很高。
In this paper we describe a close analysis of the language used in cyberbullying. We take as our corpus a collection of posts from Formspring.me. Formspring.me is a social networking site where users can ask questions of other users. It appeals primarily to teens and young adults and the cyberbullying content on the site is dense; between 7% and 14% of the posts we have analyzed contain cyberbullying content. The results presented in this article are two-fold. Our first experiments were designed to develop an understanding of both the specific words that are used by cyberbullies, and the context surrounding these words. We have identified the most commonly used cyberbullying terms, and have developed queries that can be used to detect cyberbullying content. Five of our queries achieve an average precision of 91.25% at rank 100. In our second set of experiments we extended this work by using a supervised machine learning approach for detecting cyberbullying. The machine learning experiments identify additional terms that are consistent with cyberbullying content, and identified an additional querying technique that was able to accurately assign scores to posts from Formspring.me. The posts with the highest scores are shown to have a high density of cyberbullying content.