Exploiting User Comments for Early Detection of Fake News Prior to
  Users' Commenting
Qiong Nan Qiang Sheng Juan Cao Yongchun Zhu Danding Wang Guang Yang Jintao Li Kai Shu
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.