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http://dx.doi.org/10.5859/KAIS.2021.30.4.303

How Are the Direction and the Intensity of Indirect Social Information such as Likes and Dislikes Related to the Deliberative Quality of Online News Content Comments? A Topic Diversity Analysis Using Topic Modeling  

Min, Jin Young (조선대학교 경영학부)
Lee, Ae Ri (상명대학교 경영학부)
Publication Information
The Journal of Information Systems / v.30, no.4, 2021 , pp. 303-327 More about this Journal
Abstract
Purpose The online comments on news content have become social information and are understood based on deliberative democracy. Although the related research has focused on the relationship between online comments and their deliberative quality, the social information provided by online comments consists of not only direct information such as comments themselves but also indirect information such as 'likes' and 'dislikes'. Therefore, the research on online comments and deliberative quality should study this direct and indirect information together, and the direction and the degree of the indirect information should be also considered with them. Design/methodology/approach This study distinguishes comments by the attached 'likes' and 'dislikes', identifies highly supported and highly unsupported comments by the intensity of 'likes' and 'dislikes', and investigates the relationship between their existence and the deliberative quality measured as the topic diversity. Then, we applied topic modeling to the 2,390 news articles and their 74,385 comments collected from five news sites. Findings The topic diversities of the supported and unsupported comments are related to the topic diversity of all comments but the degree of the relationship is higher in the case of supported comments. Furthermore, the existence of highly supported and unsupported comments is led to less diversity of all comments compared to the case where those comments are absent. Particularly, when only highly supported comments are present, topic diversity was lower than in the opposite case.
Keywords
Online News Content Comments; 'Likes'; 'Dislikes'; Topic Diversity; Direction and Intensity of Indirect Information; Topic Modeling;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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