Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection (2023)

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.

## introduction automatic fake news detection, which aims at distinguishing inaccurate and intentionally misleading news items from others automatically, has been a critical task for maintaining the health of the online news ecosystem @xcite . as a complement to manual verification, automatic fake news detection enables efficient filtering of fake news items from a vast news pool. such a technique has been employed by social media platforms like twitter to remove covid-19-related misleading information during the pandemic @xcite . over the past decade, most fake news detection researchers have followed a conventional paradigm of collecting a fixed dataset and randomly dividing it into training and testing sets. however, the assumption that news data subsets are independent text: yang guilan, whose admission letter was forgotten on the no. 818 bus, and now it is placed at the dispatching booth of xinxi station, please forward (from suiyang county). ## proposed framework our framework ftt is presented in figure 2 , where the instances from past consecutive time periods in the original training set are reweighted according to the forecasted topic-wise distribution for generalizing better in the upcoming time period. in the following, we first provide the problem formulation and subsequently, detail the procedures. ## evaluation we conduct experiments to answer the following evaluation questions: ## conclusion and future work we studied temporal generalization in fake news detection where a model is trained with previous news data but required to generalize well on the upcoming news data. based on the assumption that the appearance of news events on the same topic presents diverse temporal patterns, we designed a framework named ftt to capture such patterns and forecast the temporal trends at the topic level. the forecasts guided instance reweighting to improve the model's generalizability. experiments demonstrate the superiority of our framework. in the future, we plan to mine more diverse temporal patterns to further improve fake news detection in real-world temporal scenarios.