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.

通过吸收和参数化历史新闻中的评论中有用的知识，并将其注入到仅基于内容的检测模型中，我们设计了评论辅助虚假新闻检测方法 (CAS-FEND)，该方法在训练期间将有用的知识从评论感知的教师模型转移到仅基于内容的学生模型。实验证明，CAS-FEND 学生模型在早期检测方面表现出优越性，胜过所有仅基于内容的方法，甚至胜过输入 1/4 评论的方法。