Social Network Rumor Detection Method Combining Dual-Attention Mechanism With Graph Convolutional Network
Bibliographic Data
| ID | 22107554 |
|---|---|
| Authors | Xiaoyang Liu (0000-0002-8619-0356, Chongqing University of Technology), Zhengyang Zhao (0000-0003-2998-0316, Chongqing University of Technology), Yihao Zhang (0000-0003-2734-0358, Chongqing University of Technology), Chao Liu (0000-0001-9257-4519, Chongqing University of Technology), Fan Yang (0000-0003-2978-7102, Chongqing Normal University) |
| Year | 2023 |
| Volume | 10 |
| Issue | 5 |
| Pages | 2350-2361 |
| Publication date | 2023-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3184745 |
| OpenAlex | W4285276739 |
| Language | EN |
| Citations received | 11 |
| References cited | 25 |
Most rumor detection methods extract the features of rumor through two aspects of text semantics and propagation structure to achieve automatic rumor classification, while most of the existing methods do not realize that false and irrelevant interactions in the propagation structure will reduce the accuracy of rumor detection. In addition, most of the existing rumor detection methods failed to effectively extract key clues from the comments of social network users. In response to these phenomena, this article proposes a social network rumor detection method combining a dual attention mechanism and graph convolutional network (GCN) (dual-attention GCN, DA-GCN). First, build an event propagation graph; then, the GCN is used to extract the propagation structure information of each event-related microblog (tweet), and the attention mechanism is combined to suppress the false and irrelevant interactive relationships. Therefore, the anti-interference propagation structure features are extracted from the propagation graph. Second, to fully utilize the clues in users’ comments, this article makes use of the attention mechanism to fuse source microblog (tweet) with the comment–retweet information and extract interactive semantic features from it. Finally, the above two features are fused to generate a new event representation. Experimental results show that the proposed DA-GCN has an accuracy of 94.4%, 90.5%, and 90.2% on the Weibo dataset, the Twitter15 dataset, and the Twitter16 dataset, respectively, and has achieved excellent performance in the early rumor detection task, which proves that the proposed method is reasonable and effective
Data mining · Graph · Machine learning · Microblogging · Rumor · Social media · World Wide Web · Complex Network Analysis Techniques · Computer Science · Misinformation and Its Impacts · Spam and Phishing Detection · Artificial Intelligence · Theoretical Computer Science
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| Unique citing works | 11 |
|---|---|
| Citations per year | 5.5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 11 |