Rumor detection with self-supervised learning on texts and social graph

Abstract

Rumor detection has become an emerging and active research field in recent years. At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth. However, existing works on rumor detection fall short in modeling heterogeneous information, either using one single information source only (e.g., social network, or post content) or ignoring the relations among multiple sources (e.g., fusing social and content features via simple concatenation).

Therefore, they possibly have drawbacks in comprehensively understanding the rumors, and detecting them accurately. In this work, we explore contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better. Technically, we supplement the main supervised task of detection with an auxiliary self-supervised task, which enriches post representations via post self-discrimination.

Specifically, given two heterogeneous views of a post (i.e., representations encoding social patterns and semantic patterns), the discrimination is done by maximizing the mutual information between different views of the same post compared to that of other posts. We devise cluster-wise and instance-wise approaches to generate the views and conduct the discrimination, considering different relations of information sources. We term this framework as self-supervised rumor detection (SRD). Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.

Graphical abstract

Keywords

rumor detection / graph neural networks / self-supervised learning / social media

Yuan Gao received the MS degree in Electrical and Computer Engineering from University of Michigan, USA in 2019. He is now a PhD student in the School of Cyberspace Science and Technology at the University of Science and Technology of China (USTC), China. His research interest lies in fraud detection, representation learning, and graph learning

Xiang Wang is now a professor at the University of Science and Technology of China (USTC), China. He received his PhD degree from National University of Singapore, Singapore in 2019. His research interests include recommender systems, graph learning, AI explainability, and AI security. He has published some academic papers on international conferences such as NeurIPS, ICLR, KDD, WWW, SIGIR, and AAAI. He serves as a program committee member for several top conferences such as SIGIR and WWW

Xiangnan He is a professor at the University of Science and Technology of China (USTC), China. He received his PhD in Computer Science from the National University of Singapore (NUS), Singapore. His research interests span information retrieval, data mining, and multi-media analytics. He has over 80 publications that appeared in several top conferences such as SIGIR, WWW, and MM, and journals including TKDE, TOIS, and TMM. His work has received the Best Paper Award Honorable Mention in WWW 2018 and ACM SIGIR 2016. He is in the editorial board of journals including Frontiers in Big Data, AI Open. Moreover, he has served as the PC chair of CCIS 2019 and SPC/PC member for several top conferences including SIGIR, WWW, KDD, MM, WSDM, ICML, etc., and the regular reviewer for journals including TKDE, TOIS, TMM

Huamin Feng is now a professor at Beijing Electronic Science and Technology Institute, China. He received his PhD dergree from National University of Singapore, Singapore in 2005. His research interests include multimedia semantic analysis, recommend system, and Web content analysis. He has published some academic papers on international conferences such as WWW, SIGIR, and MMM

Yongdong Zhang (Senior Member, IEEE) received the PhD degree in electronic engineering from Tianjin University, China in 2002. He is currently a Professor with the University of Science and Technology of China. He has authored more than 100 refereed journal and conference papers. His current research interests include multimedia content analysis and understanding, multimedia content security, video encoding, and streaming media technology. He was a recipient of the Best Paper Award in PCM2013, ICIMCS 2013, and ICME 2010; and the Best Paper Candidate in ICME 2011. He serves as an Editorial Board Member for Multimedia Systems journal and Neurocomputing

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2020AAA0106000), the National Natural Science Foundation of China (Grant Nos. U21B2026, 62121002), and the CCCD Key Lab of Ministry of Culture and Tourism.

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