User Perceptions of AI-Based Comment Filtering Technology

Bibliographic Data

ID3756174
AuthorsYu Won Oh (0000-0001-6258-103X, Myongji University), Chong Hyun Park (0000-0003-4666-6994, Sungkyunkwan University, corresponding author)
Year2024
Volume68
Issue10
Pages1308-1324
Publication date2024-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAmerican Behavioral Scientist (JOURNAL)
Journal identifiersISSN: 0002-7642 • E-ISSN: 1552-3381
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/00027642231174331
OpenAlexW4377098624
LanguageEN
Citations received3
References cited50

As artificial intelligence (AI)-powered technology enables the efficient processing of large volumes of comments, more companies, including news publications, are experimenting with and adopting AI moderation to manage their commenting platforms. However, the resulting user experiences have been largely underexplored in relation to these technical advances in comments section management. This study used an experiment to examine the impact of AI filters on individuals' perceptions about comments sections (i.e., bias, credibility, positive and negative affect, and use intention). The findings indicate that AI moderation had statistically significant impacts on perceived credibility and use intention. Beyond the main effects, priming with a deceptive comment issue moderated the impacts of comment filtering on user perceptions as well

Affect (linguistics) · Credibility · Data mining · Filter (signal processing) · Moderation · Perception · Political science · Priming (agriculture) · Relation (database) · Computer Science · Hate Speech and Cyberbullying Detection · Misinformation and Its Impacts · Psychology · Sentiment Analysis and Opinion Mining · Social Psychology

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