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When reading news articles on social networking services and news sites,readers can view comments marked by other people on these articles. By readingthese comments, a reader can understand the public opinion about the news, andit is often helpful to grasp the overall picture of the news. However, thesecomments often contain offensive language that readers do not prefer to read.This study aims to predict such offensive comments to improve the quality ofthe experience of the reader while reading comments. By considering thediversity of the readers' values, the proposed method predicts offensive newscomments for each reader based on the feedback from a small number of newscomments that the reader rated as "offensive" in the past. In addition, we useda machine learning model that considers the characteristics of the commentersto make predictions, independent of the words and topics in news comments. Theexperimental results of the proposed method show that prediction can bepersonalized even when the amount of readers' feedback data used in theprediction is limited. In particular, the proposed method, which considers thecommenters' characteristics, has a low probability of false detection ofoffensive comments.

Personalized Prediction of Offensive News Comments by Considering the Characteristics of Commenters