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This study investigates how fake news uses a thumbnail for a news articlewith a focus on whether a news article's thumbnail represents the news contentcorrectly. A news article shared with an irrelevant thumbnail can misleadreaders into having a wrong impression of the issue, especially in social mediaenvironments where users are less likely to click the link and consume theentire content. We propose to capture the degree of semantic incongruity in themultimodal relation by using the pretrained CLIP representation. From asource-level analysis, we found that fake news employs a more incongruous imageto the main content than general news. Going further, we attempted to detectnews articles with image-text incongruity. Evaluation experiments suggest thatCLIP-based methods can successfully detect news articles in which the thumbnailis semantically irrelevant to news text. This study contributes to the researchby providing a novel view on tackling online fake news and misinformation. Codeand datasets are available athttps://github.com/ssu-humane/fake-news-thumbnail.

How does fake news use a thumbnail? CLIP-based Multimodal Detection on the Unrepresentative News Image