Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection

Li Gao, Lingyun Song, Jie Liu, Bolin Chen, Xuequn Shang


Abstract
Fake news detection is a challenging problem due to its tremendous real-world political and social impacts. Recent fake news detection works focus on learning news features from News Propagation Graph (NPG). However, little attention is paid to the issues of both authenticity of the relationships and topology imbalance in the structure of NPG, which trick existing methods and thus lead to incorrect prediction results. To tackle these issues, in this paper, we propose a novel Topology imbalance and Relation inauthenticity aware Hierarchical Graph Attention Networks (TR-HGAN) to identify fake news on social media. Specifically, we design a new topology imbalance smoothing strategy to measure the topology weight of each node. Besides, we adopt a hierarchical-level attention mechanism for graph convolutional learning, which can adaptively identify the authenticity of relationships by assigning appropriate weights to each of them. Experiments on real-world datasets demonstrate that TR-HGAN significantly outperforms state-of-the-art methods.
Anthology ID:
2022.coling-1.415
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4687–4696
Language:
URL:
https://aclanthology.org/2022.coling-1.415
DOI:
Bibkey:
Cite (ACL):
Li Gao, Lingyun Song, Jie Liu, Bolin Chen, and Xuequn Shang. 2022. Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4687–4696, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection (Gao et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.415.pdf