Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection
Mengzhu Sun, Xi Zhang, Jianqiang Ma, Yazheng Liu
Abstract
Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though a set of rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent relationships among images and texts. Moreover, they also fail to find a powerful way to spot the inconsistency information among the post contents and background knowledge. Motivated by the intuition that rumors are more likely to have inconsistency information in semantics, a novel Knowledge-guided Dual-inconsistency network is proposed to detect rumors with multimedia contents. It can capture the inconsistent semantics at the cross-modal level and the content-knowledge level in one unified framework. Extensive experiments on two public real-world datasets demonstrate that our proposal can outperform the state-of-the-art baselines.
BibTeX
@inproceedings{sun-etal-2021-inconsistency-matters,
title = "Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection",
author = "Sun, Mengzhu and
Zhang, Xi and
Ma, Jianqiang and
Liu, Yazheng",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.122/",
doi = "10.18653/v1/2021.findings-emnlp.122",
pages = "1412--1423"
}