论文标题
部分可观测时空混沌系统的无模型预测
How does fake news use a thumbnail? CLIP-based Multimodal Detection on the Unrepresentative News Image
论文作者
论文摘要
这项研究调查了假新闻如何使用缩略图作为新闻文章，重点是新闻文章的缩略图是否正确代表新闻内容。与无关的缩略图分享的新闻文章可能会误导读者对问题的印象错误，尤其是在用户单击链接并消耗整个内容的社交媒体环境中。我们建议通过使用验证的剪辑表示，捕获多模式关系中语义不一致的程度。从来源级别的分析中，我们发现假新闻比一般新闻采用了主要内容的不协调图像。进一步，我们试图以图像文本不一致来检测新闻文章。评估实验表明，基于夹的方法可以成功检测到与新闻文本无关的缩略图无关的新闻文章。这项研究通过提供有关解决在线虚假新闻和错误信息的新颖观点来为这项研究做出的贡献。代码和数据集可在https://github.com/ssu-humane/fake-news-thumbnail上找到。
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.
