Exploiting Conversation-Branch-Tweet HyperGraph Structure to Detect Misinformation on Social Media

• Published in: ACM transactions on knowledge discovery from data 2023-11, Vol.18 (2), p.1-20, Article 33
• Main Authors: Li, Fangfang, Liu, Zhi, Duan, Junwen, Mao, Xingliang, Shi, Heyuan, Zhang, Shichao
• Format: Article
• Language: English
• Subjects: Computing methodologies; Information systems; Natural language processing; Neural networks; Social networks
• ISSN: 1556-4681; 1556-472X
• DOI: 10.1145/3610297

The spread of misinformation on social media is a serious issue that can have negative consequences for public health and political stability. While detecting and identifying misinformation can be challenging, many attempts have been made to address this problem. However, traditional models that focus on pairwise relationships on misinformation propagation paths may not be effective in capturing the underlying connections among multiple tweets. To address this limitation, the proposed “Conversation-Branch-Tweet” hypergraph convolutional network (CBT-HGCN) uses a hypergraph to represent the internal structure and content of tweet data, with tweets and their replies viewed as nodes and hyperedges, respectively. The model first pre-processes the tweets of a conversation and then uses a pre-trained model as an encoder to extract node information. Finally, a hypergraph convolution network is used as an information fuser for classification. Experimental results on three benchmark datasets (Twitter15, Twitter16, and Pheme) show that the proposed model outperforms several strong baseline models and achieves state-of-the-art performance. This indicates that the CBT-HGCN approach is effective in detecting and identifying misinformation on social media by capturing the underlying connections among multiple tweets.