Abstract
Massive false rumors emerging along with breaking news or trending topics severely hinder the truth. Existing rumor detection approaches achieve promising performance on the yesterday’s news, since there is enough corpus collected from the same domain for model training. However, they are poor at detecting rumors about unforeseen events especially those propagated in different languages due to the lack of training data and prior knowledge (i.e., low-resource regimes). In this paper, we propose an adversarial contrastive learning framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced. Our model explicitly overcomes the restriction of domain and/or
language usage via language alignment and a novel supervised contrastive training paradigm. Moreover, we develop an adversarial augmentation mechanism to further enhance the robustness of low-resource rumor representation. Extensive experiments conducted on two low-resource datasets collected from real-world microblog platforms demonstrate that our framework achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.
language usage via language alignment and a novel supervised contrastive training paradigm. Moreover, we develop an adversarial augmentation mechanism to further enhance the robustness of low-resource rumor representation. Extensive experiments conducted on two low-resource datasets collected from real-world microblog platforms demonstrate that our framework achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.
Original language | English |
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Title of host publication | Findings of the Association for Computational Linguistics: NAACL 2022 |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 2543-2556 |
Number of pages | 14 |
ISBN (Electronic) | 9781955917766 |
ISBN (Print) | 9781955917766 |
Publication status | Published - 10 Jul 2022 |
Event | Findings of the Association for Computational Linguistics: NAACL 2022 - Seattle, United States Duration: 10 Jul 2022 → 15 Jul 2022 |
Publication series
Name | Findings of the Association for Computational Linguistics: NAACL 2022 - Findings |
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Conference
Conference | Findings of the Association for Computational Linguistics: NAACL 2022 |
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Country/Territory | United States |
City | Seattle |
Period | 10/07/22 → 15/07/22 |
Scopus Subject Areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics