No Place to Hide
Dual Deep Interaction Channel Network for Fake News Detection With Data Augmentation
Bibliographic Data
| ID | 22107397 |
|---|---|
| Authors | Biwei Cao (0000-0002-3375-404X, Southeast University), Jiuxin Cao (0000-0002-2448-6717, Southeast University), Lulu Hua (0009-0006-6597-7156, Southeast University), Xin Guan (0000-0002-1088-2639, Southeast University), Bo Liu (0000-0002-6536-517X, Southeast University), Chenglong He (0009-0008-1993-0589, Research Center, Nanjing LES Cybersecurity and Information Technology Research Institute Company LTD., Nanjing, China), Jie Gui (0000-0001-8980-4707, Southeast University), James Tin-Yau Kwok (0000-0002-4828-8248, Hong Kong University of Science and Technology) |
| Year | 2025 |
| Volume | 12 |
| Issue | 6 |
| Pages | 4013-4024 |
| Publication date | 2025-12-01 |
| 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.2025.3571024 |
| OpenAlex | W4411142991 |
| Language | EN |
| References cited | 25 |
Online social network has emerged as a prominent place for the propagation of fake news due to its low cost of information dissemination. Although the existing methods have made many attempts in news content and propagation structure, the detection of fake news is still facing two challenges: one is how to mine the unique key features and evolution patterns, and the other is how to tackle the problem of small samples to build the high-performance model. Different from popular methods, which take full advantage of the propagation topology structure, in this article, we propose a novel framework for fake news detection from perspectives of semantics, emotion and data enhancement. The semantic and emotional features of news and comments, the inconsistent emotion between news and news participants as well as the emotion evolution features in comments are fused by the designed dual deep interaction channel network to obtain a more comprehensive and fine-grained news representation. Meanwhile, with the construction of large language model (LLM) prompt, a LLM-based data enhancement module is used to obtain more diverse labeled data of high quality filtered by confidence, further improving the performance of the classification model. Experiments show that the proposed approach outperforms the state-of-the-art methods
Art · Computer network · Computer security · Fake news · Internet privacy · Telecommunications · Advanced Malware Detection Techniques · Computer Science · Misinformation and Its Impacts · Spam and Phishing Detection
| Citation velocity | historical |
|---|---|
| Highly cited | No |