  This study investigates how fake news uses a thumbnail for a news article
with a focus on whether a news article's thumbnail represents the news content
correctly. A news article shared with an irrelevant thumbnail can mislead
readers into having a wrong impression of the issue, especially in social media
environments where users are less likely to click the link and consume the
entire content. We propose to capture the degree of semantic incongruity in the
multimodal relation by using the pretrained CLIP representation. From a
source-level analysis, we found that fake news employs a more incongruous image
to the main content than general news. Going further, we attempted to detect
news articles with image-text incongruity. Evaluation experiments suggest that
CLIP-based methods can successfully detect news articles in which the thumbnail
is semantically irrelevant to news text. This study contributes to the research
by providing a novel view on tackling online fake news and misinformation. Code
and datasets are available at
https://github.com/ssu-humane/fake-news-thumbnail.
