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.
BibTeX
@inproceedings{chen-etal-2020-modeling-evolution,
title = "Modeling Evolution of Message Interaction for Rumor Resolution",
author = "Chen, Lei and
Wei, Zhongyu and
Li, Jing and
Zhou, Baohua and
Zhang, Qi and
Huang, Xuanjing",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.561/",
doi = "10.18653/v1/2020.coling-main.561",
pages = "6377--6387"
}