Abstract
Detecting fake news on social media is an urgent task. Some early studies focus on capturing authenticity information from news content, while the single information source results in limited clues. Recent studies have great concern about more clues derived from auxiliary knowledge, yet it is usually rare at the early stage of news propagation. Furthermore, such studies, with more attention on the intra-news properties of individual news, would impair the performance on newly emerged events. To alleviate these issues, we propose a novel Inter-News Relation Mining (INRM) framework to mine inter-news relations. Whether for scenarios with little auxiliary knowledge or newly emerged events, INRM can provide more effective credibility clues for verifying th e truth of news. Experiments illustrate that INRM outperforms state-of-the-art methods on both conventional tasks and newly emerged event tasks.
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Acknowledgements
This work is supported by the Youth Innovation Promotion Association, Chinese Academy of Sciences (No. 2020163).
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Zhang, L., Xu, H., Shi, F., Yan, C., Xu, Y. (2023). Broaden Your Horizons: Inter-news Relation Mining for Fake News Detection. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13946. Springer, Cham. https://doi.org/10.1007/978-3-031-30678-5_4
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DOI: https://doi.org/10.1007/978-3-031-30678-5_4
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