Both accuracy and timeliness are key factors in detecting fake news on social media. However, most existing methods encounter an accuracy-timeliness dilemma: Content-only methods guarantee timeliness but perform moderately because of limited available information, while social context-based ones generally perform better but inevitably lead to latency because of social context accumulation needs. To break such a dilemma, a feasible but not well-studied solution is to leverage social contexts (e.g., comments) from historical news for training a detection model and apply it to newly emerging news without social contexts. This requires the model to (1) sufficiently learn helpful knowledge from social contexts, and (2) be well compatible with situations that social contexts are available or not. To achieve this goal, we propose to absorb and parameterize useful knowledge from comments in historical news and then inject it into a content-only detection model. Specifically, we design the Comments Assisted Fake News Detection method (CAS-FEND), which transfers useful knowledge from a comments-aware teacher model to a content-only student model during training. The student model is further used to detect newly emerging fake news. Experiments show that the CAS-FEND student model outperforms all content-only methods and even those with 1/4 comments as inputs, demonstrating its superiority for early detection.

翻译：准确性和时效性是社交媒体上假新闻检测的关键因素。然而，现有方法大多面临准确性-时效性困境：纯内容方法保证了时效性，但因可用信息有限而表现平平；基于社交语境的方法通常表现更优，却因需要积累社交语境而不可避免地导致延迟。为突破这一困境，一种可行但尚未充分研究的解决方案是：利用历史新闻中的社交语境（如评论）训练检测模型，并将其应用于尚无社交语境的新兴新闻。这要求模型（1）从社交语境中充分学习有用知识，且（2）能良好兼容有无社交语境的不同场景。为实现该目标，我们提出从历史新闻评论中吸收有用知识并参数化，随后将其注入纯内容检测模型。具体而言，我们设计了评论辅助假新闻检测方法（CAS-FEND），在训练阶段将评论感知教师模型的有用知识迁移至纯内容学生模型。该学生模型进一步用于检测新兴假新闻。实验表明，CAS-FEND学生模型优于所有纯内容方法，甚至超越以1/4评论作为输入的方法，证明了其在早期检测中的优越性。