Modeling Evolution of Message Interaction for Rumor Resolution

Lei Chen, Zhongyu Wei, Jing Li, Baohua Zhou, Qi Zhang, Xuanjing Huang


Abstract
Previous work for rumor resolution concentrates on exploiting time-series characteristics or modeling topology structure separately. However, how local interactive pattern affects global information assemblage has not been explored. In this paper, we attempt to address the problem by learning evolution of message interaction. We model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. Moreover, we capture the variation of message interaction using a hierarchical framework to better integrate information flow of a rumor cascade. Experiments on PHEME dataset demonstrate our proposed model achieves higher accuracy than existing methods.
Anthology ID:
2020.coling-main.561
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6377–6387
Language:
URL:
https://aclanthology.org/2020.coling-main.561
DOI:
10.18653/v1/2020.coling-main.561
Bibkey:
Cite (ACL):
Lei Chen, Zhongyu Wei, Jing Li, Baohua Zhou, Qi Zhang, and Xuanjing Huang. 2020. Modeling Evolution of Message Interaction for Rumor Resolution. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6377–6387, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Modeling Evolution of Message Interaction for Rumor Resolution (Chen et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.561.pdf