Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake
  News Detection
Beizhe Hu Qiang Sheng Juan Cao Yongchun Zhu Danding Wang Zhengjia Wang Zhiwei Jin
Fake news detection has been a critical task for maintaining the health of
the online news ecosystem. However, very few existing works consider the
temporal shift issue caused by the rapidly-evolving nature of news data in
practice, resulting in significant performance degradation when training on
past data and testing on future data. In this paper, we observe that the
appearances of news events on the same topic may display discernible patterns
over time, and posit that such patterns can assist in selecting training
instances that could make the model adapt better to future data. Specifically,
we design an effective framework FTT (Forecasting Temporal Trends), which could
forecast the temporal distribution patterns of news data and then guide the
detector to fast adapt to future distribution. Experiments on the real-world
temporally split dataset demonstrate the superiority of our proposed framework.
The code is available at https://github.com/ICTMCG/FTT-ACL23.