{"title":"The AI Referee: How Online Interventions Shape Incivility and User Engagement in News Discussions","authors":"Georgia Kernell, Seonhye Noh","doi":"10.1177/20563051261428916","DOIUrl":null,"url":null,"abstract":"This paper seeks to understand how online interventions shape incivility and user engagement with news comments. Using a novel dataset of over 39 million news comments on Korea’s largest online news source (Naver News), we examine changes in the share of comments that are categorized as uncivil before and after the introduction of two automated interventions aimed at flagging incivility. We trained two deep learning models to categorize comments and replicate each intervention. The findings reveal significant decreases in uncivil content following each intervention. Interestingly, we find mixed effects of the interventions on total engagement: while the number of comments and commenters decreased after the first intervention, both metrics increased after the second. Examining individual-level data reveals that the aggregate reduction in incivility cuts across all users regardless of pre-intervention incivility or commenting frequency.","PeriodicalId":47920,"journal":{"name":"Social Media + Society","volume":"16 1","pages":""},"PeriodicalIF":4.9000,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social Media + Society","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1177/20563051261428916","RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMMUNICATION","Score":null,"Total":0}
引用次数: 0
Abstract
This paper seeks to understand how online interventions shape incivility and user engagement with news comments. Using a novel dataset of over 39 million news comments on Korea’s largest online news source (Naver News), we examine changes in the share of comments that are categorized as uncivil before and after the introduction of two automated interventions aimed at flagging incivility. We trained two deep learning models to categorize comments and replicate each intervention. The findings reveal significant decreases in uncivil content following each intervention. Interestingly, we find mixed effects of the interventions on total engagement: while the number of comments and commenters decreased after the first intervention, both metrics increased after the second. Examining individual-level data reveals that the aggregate reduction in incivility cuts across all users regardless of pre-intervention incivility or commenting frequency.
期刊介绍:
Social Media + Society is an open access, peer-reviewed scholarly journal that focuses on the socio-cultural, political, psychological, historical, economic, legal and policy dimensions of social media in societies past, contemporary and future. We publish interdisciplinary work that draws from the social sciences, humanities and computational social sciences, reaches out to the arts and natural sciences, and we endorse mixed methods and methodologies. The journal is open to a diversity of theoretic paradigms and methodologies. The editorial vision of Social Media + Society draws inspiration from research on social media to outline a field of study poised to reflexively grow as social technologies evolve. We foster the open access of sharing of research on the social properties of media, as they manifest themselves through the uses people make of networked platforms past and present, digital and non. The journal presents a collaborative, open, and shared space, dedicated exclusively to the study of social media and their implications for societies. It facilitates state-of-the-art research on cutting-edge trends and allows scholars to focus and track trends specific to this field of study.