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

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arxiv 2306.14728 v1 pith:BHEBOIUJ submitted 2023-06-26 cs.CL cs.AIcs.SI

Beizhe Hu , Qiang Sheng , Juan Cao , Yongchun Zhu , Danding Wang , Zhengjia Wang , Zhiwei Jin This is my paper · ORCID
classification cs.CLcs.AIcs.SI
keywords newsdatafuturetemporalpatternsadaptdetectiondistribution
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection

    cs.CL 2024-12 conditional novelty 6.0 of 10

    The authors introduce AMG, an attribution-labeled multimodal fake news benchmark with five fake patterns, plus MGCA, a clue-alignment model that outperforms baselines on it.

  2. The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News

    cs.SI 2025-05 conditional novelty 5.0 of 10

    TED uses structured pro/con debates between LLM agents plus a graph-based analysis model to detect fake news more accurately than prior methods on two benchmark datasets.

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