When reading news articles on social networking services and news sites, readers can view comments marked by other people on these articles. By reading these comments, a reader can understand the public opinion about the news, and it is often helpful to grasp the overall picture of the news. However, these comments often contain offensive language that readers do not prefer to read. This study aims to predict such offensive comments to improve the quality of the experience of the reader while reading comments. By considering the diversity of the readers' values, the proposed method predicts offensive news comments for each reader based on the feedback from a small number of news comments that the reader rated as "offensive" in the past. In addition, we used a machine learning model that considers the characteristics of the commenters to make predictions, independent of the words and topics in news comments. The experimental results of the proposed method show that prediction can be personalized even when the amount of readers' feedback data used in the prediction is limited. In particular, the proposed method, which considers the commenters' characteristics, has a low probability of false detection of offensive comments.
翻译：阅读社会网络服务和新闻网站的新闻文章时,读者可以阅读其他人对文章的评论。阅读这些评论后,读者可以理解公众对于新闻的看法,了解新闻的总体情况往往有帮助。然而,这些评论往往含有读者不愿阅读的冒犯性语言。本研究报告旨在预测这些冒犯性评论,以提高读者阅读评论时的体验质量。考虑到读者价值观的多样性,拟议方法根据读者认为过去“冒犯性”的少数新闻评论反馈,预测每个读者的冒犯性新闻评论。此外,我们使用一个机器学习模型,考虑评论者的特点,作出预测,独立于新闻评论中的文字和专题。拟议方法的实验结果显示,即使在预测中使用的读者反馈数据数量有限的情况下,预测也可以被个性化。特别是,考虑到评论者特点的拟议方法对攻击性评论进行虚假检测的可能性很低。
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