Flaming Participants Detection Using Account and Stylistic Characteristics from SNS

Abstract

Recent years have seen a rapid increase in social media network (SNS) users due to their swift growth. Consequently, people can easily engage in interactive communication with a vast and undefined audience. This has given rise to a recurring phenomenon called “flaming”, in which critical comments flood SNS. While various studies on flaming have been conducted, most of them have primarily focused on individuals receiving significant volumes of critical comments, rather than those who compose them, referred to as “flaming participants”. In this study, we examine the characteristics of flaming participants on Twitter (Although the name has now been changed to “X”, this paper still uses “Twitter” as its name) by using machine learn-ing to classify them into two groups: flaming participants and normal users. For the classification features, we utilize account information, i.e., statistical data for each account, and stylistic features of the postings, i.e., (1, n)-grams of the part-of-speech tags of the postings. Our experimental findings underscore the effectiveness of these features in identifying Twitter’s flaming participants. Additionally, our research reveals that flaming participants tend to employ quote tweets more frequently than typical users, and there are distinctive word patterns observable among flaming participants.