Abstract
The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues. In this paper, we move towards fine-grained reasoning for fake news detection by better reflecting the logical processes of human thinking and enabling the modeling of subtle clues. In particular, we propose a fine-grained reasoning framework by following the human's information-processing model, introduce a mutual-reinforcement-based method for incorporating human knowledge about which evidence is more important, and design a prior-aware bi-channel kernel graph network to model subtle differences between pieces of evidence. Extensive experiments show that our model outperforms the state-of-the-art methods and demonstrate the explainability of our approach.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 |
| Publisher | Association for the Advancement of Artificial Intelligence |
| Pages | 5746-5754 |
| Number of pages | 9 |
| ISBN (Electronic) | 1577358767, 9781577358763 |
| DOIs | |
| Publication status | Published - 30 Jun 2022 |
| Event | 36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual Duration: 22 Feb 2022 → 1 Mar 2022 https://aaai.org/Conferences/AAAI-22/ https://ojs.aaai.org/index.php/AAAI/issue/archive |
Publication series
| Name | Proceedings of the AAAI Conference on Artificial Intelligence |
|---|---|
| Publisher | AAAI Press |
| Number | 5 |
| Volume | 36 |
| ISSN (Print) | 2159-5399 |
| ISSN (Electronic) | 2374-3468 |
Conference
| Conference | 36th AAAI Conference on Artificial Intelligence, AAAI 2022 |
|---|---|
| Period | 22/02/22 → 1/03/22 |
| Internet address |
User-Defined Keywords
- Knowledge Representation And Reasoning (KRR)
- Speech & Natural Language Processing (SNLP)
- Data Mining & Knowledge Management (DMKM)
- Machine Learning (ML)
