@inproceedings{hu-etal-2023-learn,
    title = "Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection",
    author = "Hu, Beizhe  and
      Sheng, Qiang  and
      Cao, Juan  and
      Zhu, Yongchun  and
      Wang, Danding  and
      Wang, Zhengjia  and
      Jin, Zhiwei",
    editor = "Sitaram, Sunayana  and
      Beigman Klebanov, Beata  and
      Williams, Jason D",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-industry.13",
    doi = "10.18653/v1/2023.acl-industry.13",
    pages = "116--125",
    abstract = "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.",
}

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.
Hu, Beizhe Sheng, Qiang Cao, Juan Zhu, Yongchun Wang, D ing Wang, Zhengjia Jin, Zhiwei
Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection