  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.
