MMAdapt: A Knowledge-guided Multi-source Multi-class Domain Adaptive Framework for Early Health Misinformation Detection

Lanyu Shang, Yang Zhang, Bozhang Chen, Ruohan Zong, Zhenrui Yue, Huimin Zeng, Na Wei, Dong Wang

Abstract: This paper studies a critical problem of emergent health misinformation detection, aiming to mitigate the spread of misinformation in emergent health domains to support well-informed healthcare decisions towards a Web for good health. Our work is motivated by the lack of timely resources (e.g., medical knowledge, annotated data) during the initial phases of an emergent health event or topic. In this paper, we develop a multi-source domain adaptive framework that jointly exploits medical knowledge and annotated data from different high-resource source domains (e.g., cancer, COVID-19) to detect misleading posts in an emergent target domain (e.g., mpox, polio). Two important challenges exist in developing our solution: 1) how to accurately detect the partially misleading and unverifiable content in an emergent target domain? 2) How to identify the conflicting knowledge facts from different source domains to accurately detect emergent misinformation in the target domain? To address these challenges, we develop MMAdapt, a multi-source multi-class domain adaptive misinformation detection framework that effectively explores diverse knowledge facts from different source domains to accurately detect not only the outright misleading but also the partially misleading or unverifiable posts on the Web. Extensive experimental results on four real-world misinformation datasets demonstrate that MMAdapt substantially outperforms state-of-the-art baselines in accurately detecting misinformation in an emergent health domain.