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.

翻译：假新闻检测对于维护在线新闻生态系统的健康至关重要。然而，现有研究鲜有考虑新闻数据在实践中快速演化所导致的时间偏移问题，导致模型在历史数据上训练、未来数据上测试时性能显著下降。本文观察到，同一主题的新闻事件在时间维度上的呈现模式具有可辨别的规律，并假设此类模式能够辅助选择训练样本，从而使模型更好地适应未来数据。具体而言，我们设计了一种高效框架FTT（时间趋势预测），该框架能够预测新闻数据的时间分布模式，进而引导检测器快速适应未来分布。在真实世界的时间分割数据集上的实验证明了所提框架的优越性。代码已开源至https://github.com/ICTMCG/FTT-ACL23。