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About this paper
Qiong Nan, Qiang Sheng, Juan Cao, Yongchun Zhu, Danding Wang, Guang Yang, Jintao Li
A knowledge distillation framework enables content-only models to detect fake news early with high accuracy by leveraging semantic and emotional knowledge extracted from historical user comments. This approach allows newly emerging news to be classified effectively without waiting for social context to accumulate, outperforming existing content-only and many comment-aware detection methods.
Key insights
- Knowledge distillation effectively transfers implicit and explicit wisdom from historical user comments to a content-only model, bridging the 'information gap' for new articles.
- Deconstructing social context into semantic and emotional knowledge, and adaptively distilling these components, significantly enhances early fake news detection performance.
- A 'virtual social emotion' can be predicted from news content, allowing the content-only student model to anticipate collective emotional responses without requiring real-time user comments.
Summary by alphaXiv.
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