Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection

Wei-Yao Wang, Yu-Chieh Chang, Wen-Chih Peng


Abstract
With the improvements in generative models, the issues of producing hallucinations in various domains (e.g., law, writing) have been brought to people’s attention due to concerns about misinformation. In this paper, we focus on neural fake news, which refers to content generated by neural networks aiming to mimic the style of real news to deceive people. To prevent harmful disinformation spreading fallaciously from malicious social media (e.g., content farms), we propose a novel verification framework, Style-News, using publisher metadata to imply a publisher’s template with the corresponding text types, political stance, and credibility. Based on threat modeling aspects, a style-aware neural news generator is introduced as an adversary for generating news content conditioning for a specific publisher, and style and source discriminators are trained to defend against this attack by identifying which publisher the style corresponds with, and discriminating whether the source of the given news is human-written or machine-generated. To evaluate the quality of the generated content, we integrate various dimensional metrics (language fluency, content preservation, and style adherence) and demonstrate that Style-News significantly outperforms the previous approaches by a margin of 0.35 for fluency, 15.24 for content, and 0.38 for style at most. Moreover, our discriminative model outperforms state-of-the-art baselines in terms of publisher prediction (up to 4.64%) and neural fake news detection (+6.94% 31.72%). We plan to release our Style-News publicly, with the aim of improving neural fake news detection.
Anthology ID:
2024.eacl-long.92
Volume:
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Yvette Graham, Matthew Purver
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1531–1541
Language:
URL:
https://aclanthology.org/2024.eacl-long.92
DOI:
Bibkey:
Cite (ACL):
Wei-Yao Wang, Yu-Chieh Chang, and Wen-Chih Peng. 2024. Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1531–1541, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection (Wang et al., EACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.eacl-long.92.pdf
Software:
 2024.eacl-long.92.software.zip