Open Access
| Issue |
ITM Web Conf.
Volume 88, 2026
The 2026 International Conference on Artificial Intelligence, Big Data and Computer Science (AIBDCS 2026)
|
|
|---|---|---|
| Article Number | 01024 | |
| Number of page(s) | 4 | |
| Section | Artificial Intelligence, Big Data and Computer Science | |
| DOI | https://doi.org/10.1051/itmconf/20268801024 | |
| Published online | 27 July 2026 | |
- Y. Shi, X. Zhang, Y. Shang, N. Yu, Don’t Be Misled by Emotion! Disentangle Emotions and Semantics for Cross-Language and Cross-Domain Rumor Detection. IEEE Trans. Big Data. 10, 249–259 (2023) https://doi.org/10.1109/TBDATA.2023.3334634 [Google Scholar]
- S. Chen, X. Chen, D. Miao, H. Zhang, X. Qin, P. Lu, ADA-UDA: A transferable transformer framework for rumor detection using Adversarial Domain Alignment within Unsupervised Domain Adaptation. Expert Syst. Appl. 261, 125487 (2025) https://doi.org/10.1016/j.eswa.2024.125487 [Google Scholar]
- H. Ran, X. Li, Z. Zhang, Label-aware learning to enhance unsupervised cross-domain rumor detection. J. Netw. Comput. Appl. 235, 104084 (2025) https://doi.org/10.1016/j.jnca.2024.104084 [Google Scholar]
- X. Pu, H. Wu, X. Bi, Y. Wu, X. Gao, DEAR: Disentangled Event-Agnostic Representation Learning for Early Fake News Detection. Trans. Assoc. Comput. Linguist. 13, 343–356 (2025) https://doi.org/10.1162/tacl_a_00743 [Google Scholar]
- J. Li, Y. Gao, Y. Yang, Y. Bai, X. Zhou, Y. Li, H. Sun, Y. Liu, X. Si, Y. Ye, Y. Wu, Y. Lin, B. Xu, B. Ren, C. Feng, H. Huang, Fundamental Capabilities and Applications of Large Language Models: A Survey. ACM Comput. Surv. 58, 3735632 (2025) https://doi.org/10.1145/3735632 [Google Scholar]
- L. Locowic, A. Monteverdi, E. Mendoza. Synthetic Data Generation from Real Data Sources using Monte Carlo Tree Search and Large Language Models. Authorea. September 17 (2024) https://doi.org/10.22541/au.172660477.72210139/v1 [Google Scholar]
- S. Liu, M. Lu, Z. Huang, Z. He, L. Liu, Z. Sun, D. Li, MONTROSE: LLM-driven Monte Carlo Tree Search Self-Refinement for Cross-Domain Rumor Detection, in Findings of the Association for Computational Linguistics: ACL 2025, Vienna, Austria, (2025), 21475–21487 https://doi.org/10.18653/v1/2025.findings-acl.1106 [Google Scholar]
- Y. Gong, S. Hu, H. Zhang, Cross-domain Rumor Detection via Test-Time Adaptation and Large Language Models, in Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Suzhou, China, (2025), 8051–8066 https://doi.org/10.18653/v1/2025.emnlp-main.407 [Google Scholar]
- Y. Xiao, J. Yang, W. Zhao, Q. Li , Y. Pang, Cross-Domain Social Rumor-Propagation Model Based on Transfer Learning. IEEE Trans. Neural Netw. Learn. Syst. 36, 6529–6543 (2024) https://doi.org/10.1109/TNNLS.2024.3405991 [Google Scholar]
- Z. Liu, K. Yang, Q. Xie, C. de Kock, S. Ananiadou, E. Hovy, RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information, in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Vienna, Austria, (2025), 16508–16523 https://doi.org/10.18653/v1/2025.acl-long.806 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.