A Unified Propagation Forest-based Framework for Fake News Detection

Lingwei Wei, Dou Hu, Yantong Lai, Wei Zhou, Songlin Hu


Abstract
Fake news’s quick propagation on social media brings severe social ramifications and economic damage. Previous fake news detection usually learn semantic and structural patterns within a single target propagation tree. However, they are usually limited in narrow signals since they do not consider latent information cross other propagation trees. Motivated by a common phenomenon that most fake news is published around a specific hot event/topic, this paper develops a new concept of propagation forest to naturally combine propagation trees in a semantic-aware clustering. We propose a novel Unified Propagation Forest-based framework (UniPF) to fully explore latent correlations between propagation trees to improve fake news detection. Besides, we design a root-induced training strategy, which encourages representations of propagation trees to be closer to their prototypical root nodes. Extensive experiments on four benchmarks consistently suggest the effectiveness and scalability of UniPF.
Anthology ID:
2022.coling-1.244
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:
2769–2779
Language:
URL:
https://aclanthology.org/2022.coling-1.244
DOI:
Bibkey:
Cite (ACL):
Lingwei Wei, Dou Hu, Yantong Lai, Wei Zhou, and Songlin Hu. 2022. A Unified Propagation Forest-based Framework for Fake News Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2769–2779, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Unified Propagation Forest-based Framework for Fake News Detection (Wei et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.244.pdf