Unpacking Multimodal Fact-Checking

Features and Engagement of Fact-Checking Videos on Chinese TikTok (Douyin)

Datos Bibliográficos

ID2414541
AutoresYingdan Lu (0000-0002-9955-6070, Stanford University, autor de correspondencia), Cuihua Shen (0000-0003-3871-8256, University of California, Davis)
Año2023
Volumen9
Número1
Fecha de publicación2023-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSocial Media + Society (JOURNAL)
Identificadores de la revistaISSN: 2056-3051 • E-ISSN: 2056-3051
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/20563051221150406
OpenAlexW4321120732
IdiomaEN
Citas recibidas32
Referencias citadas72

As fact-checking videos increasingly circulate on video-sharing platforms, more research is needed to understand the prevalent features of such videos and how they are associated with audience engagement. Drawing from the literature on fact-checking, communication, marketing, and computer science, we identified eight audiovisual features as well as seven persuasive strategies that are most relevant to fact-checking videos. Using a hybrid video analysis framework combining both automated and manual content analysis, we examined 4,309 fact-checking videos on Douyin, the Chinese version of TikTok. We found that fact-checking videos on Douyin tended to have higher brightness, less cool color dominance, and faster tempo than non-fact-checking videos from the same accounts and Douyin Trending videos, and frequently used persuasive strategies like clickbait and humor. Through feature clustering, we established three types of fact-checking videos on Douyin-long storytelling cartoons, short stimulating videos, and short authoritative videos. We found that several audiovisual features and persuasive strategies were associated with audience engagement, such as likes, comments, and reshares. This study sheds light on the common practices of fact-checking videos in Chinese cyberspace, extends the current image-as-data paradigm to fact-checking videos, and helps fact-checkers make evidence-based decisions on content creation

Linguistics · Multimedia · Narrative · Storytelling · Unpacking · Computer Science · Media Influence and Health · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining