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首页> 外文会议>IEEE Conference on Recent Advances in Intelligent Computational Systems >Combating Online Hate: A Comparative Study on Identification of Hate Speech and Offensive Content in Social Media Text
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Combating Online Hate: A Comparative Study on Identification of Hate Speech and Offensive Content in Social Media Text

机译：对抗在线仇恨：社会媒体文本识别仇恨言论和攻击性含量的比较研究

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This paper addresses the important issue of rising hate and offensive comments against individuals or communities on social media. Such behaviour has become pervasive in social media where people are easily able to vent out their hatred and reach out to a large number of people, which they may not consider in the physical world. One of the most effective solution for tackling this enigmatic problem is the use of computational techniques to identify such hateful and offensive content and to take action against it. The current work focuses on detecting hate speech and offensive content in Indo-European languages keeping English on the frontline since it is the most widely used language on the Internet. The datasets used for the experiment are obtained from CrowdFlower and FIRE-2019 task on Identifying Hate Speech and Offensive Content in Social Media Text (HASOC). The paper provides a comparative analysis and explores the effectiveness of the TF-IDF approach and various word embedding-based approaches for the classification task on both the datasets. The evaluation measures are accuracy, precision, recall and F1-score.
机译：本文涉及对社交媒体上的个人或社区的讨厌和冒犯性评论的重要问题。这些行为在社交媒体中变得普遍存在，人们很容易能够发泄他们的仇恨并伸向大量的人，他们可能在物质世界中不考虑。解决这种神秘问题的最有效的解决方案之一是使用计算技术来识别这种可恶和令人反感的内容并采取行动。目前的工作侧重于检测在欧洲语言中的仇恨言论和冒犯内容，以便在前线上进行英语，因为它是互联网上最广泛使用的语言。用于实验的数据集是从众人和Fire-2019任务中获得，即在社交​​媒体文本（HASOC）中识别仇恨言论和冒犯性内容。本文提供了比较分析，探讨了TF-IDF方法的有效性以及基于词的嵌入基于数据集的分类任务的方法。评估措施是准确性，精确，召回和F1分数。

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