Abstract
During the outbreak of pandemics, various rumors emerged one after another, causing widespread panic and confusion. Detecting these rumors in time is crucial. This paper focuses on detecting COVID-19 rumors. However, while many graph-based methods are utilized to analyze the propagation structure, they rarely simultaneously consider the internal topology of the source post. Additionally, existing methods are reliant heavily on data. They generally perform poorly due to a lack of training data. To address these issues, we propose a novel method based on Heterogeneous Graph Convolutional Network with Cross-domain Contrastive Learning (HGCCL). Specifically, we construct a hierarchical heterogeneous graph that incorporates the local semantic structure of a source post and the global propagation structure, enabling us to capture rich structural and semantic information. Moreover, rumors of the same category share similarities even across different domains. Hence, we introduce Cross-domain Contrastive Learning (CCL) to learn domain-invariant features by aligning features from open-domain datasets with COVID-19 datasets in corresponding category spaces. To validate the effectiveness of HGCCL, we conducted experiments on two COVID-19 datasets sourced from Twitter and Weibo. Experimental results demonstrate that HGCCL outperforms state-of-the-art methods.
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Acknowledgments
The authors would like to thank the two anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (Nos. 62276177 and 62376181), and Key R&D Plan of Jiangsu Province (BE2021048).
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Tang, S., Qian, Z., Liu, C., Li, P., Zhu, Q. (2024). COVID-19 Rumor Detection Based on Heterogeneous Graph Convolutional Network with Cross-Domain Contrastive Learning. In: Huang, DS., Si, Z., Zhang, C. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science(), vol 14878. Springer, Singapore. https://doi.org/10.1007/978-981-97-5672-8_19
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