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

TL;DR

This paper presents a personalized machine learning approach to predict offensive news comments by considering individual reader preferences and commenter characteristics, aiming to enhance user experience on social news platforms.

Contribution

It introduces a novel method that personalizes offensive comment prediction using limited feedback and commenter features, independent of comment content.

Findings

Effective personalization with limited feedback data

Reduced false positive rate in offensive comment detection

Commenter characteristics improve prediction accuracy

Abstract

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.