@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection
%A Hu, Beizhe
%A Sheng, Qiang
%A Cao, Juan
%A Zhu, Yongchun
%A Wang, Danding
%A Wang, Zhengjia
%A Jin, Zhiwei
%Y Sitaram, Sunayana
%Y Beigman Klebanov, Beata
%Y Williams, Jason D.
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F hu-etal-2023-learn
%X 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.
%R 10.18653/v1/2023.acl-industry.13
%U https://aclanthology.org/2023.acl-industry.13
%U https://doi.org/10.18653/v1/2023.acl-industry.13
%P 116-125
Markdown (Informal)
[Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection](https://aclanthology.org/2023.acl-industry.13) (Hu et al., ACL 2023)
ACL
- Beizhe Hu, Qiang Sheng, Juan Cao, Yongchun Zhu, Danding Wang, Zhengjia Wang, and Zhiwei Jin. 2023. Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), pages 116–125, Toronto, Canada. Association for Computational Linguistics.