| 标题 |
 A Survey of Multimodal Fake News Detection: A Cross-Modal Interaction Perspective 多模态假新闻检测综述：跨模态交互视角
相关领域
透视图（图形）
情态动词
计算机科学
人工智能
材料科学
高分子化学
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| DOI | |
| 其它 | The growth of social media platforms has made it easier for fake news to spread, which poses a significant threat to authoritative news outlets, politics, and public health. Manual verification of the massive amount of online information has proven to be a daunting task, which has led to the growing interest in automatic fake news detection. Some methods that rely on news text, images, external knowledge, social contexts, or propagation graphs have demonstrated good performance. In contrast to earlier studies that focused solely on the unimodal news textual information, recent works have integrated multimodal features from various granularities, such as words, visual semantic regions, and multimodal entities, to more effectively leverage news content and align with human reading habits. However, a comprehensive review of Multimodal Fake News Detection (MFND) is still lacking, prompting our aim to complement this topic. Specifically, |
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