论文标题
通过考虑评论者的特征来对进攻新闻评论的个性化预测
Personalized Prediction of Offensive News Comments by Considering the Characteristics of Commenters
论文作者
论文摘要
在阅读有关社交网络服务和新闻网站的新闻文章时，读者可以查看其他人对这些文章的评论。通过阅读这些评论，读者可以理解有关新闻的公众舆论，并且掌握新闻的整体情况通常会有所帮助。但是，这些评论通常包含读者不喜欢阅读的令人反感的语言。这项研究旨在预测这种令人反感的评论，以提高阅读评论时读者体验的质量。通过考虑读者价值观的多样性，该提议的方法是根据少数新闻评论的反馈来预测每个读者的进攻新闻评论，这些新闻评论过去将读者评为“令人反感”。此外，我们使用了一种机器学习模型，该模型考虑了评论者的特征来做出预测，而与新闻评论中的单词和主题无关。提出方法的实验结果表明，即使预测中使用的读者反馈数据的数量受到限制，也可以个性化预测。特别是，考虑到评论者特征的提议方法的错误检测可能性很小。
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.
