Demystifying Neural Fake News via Linguistic Feature-Based Interpretation

Ankit Aich, Souvik Bhattacharya, Natalie Parde


Abstract
The spread of fake news can have devastating ramifications, and recent advancements to neural fake news generators have made it challenging to understand how misinformation generated by these models may best be confronted. We conduct a feature-based study to gain an interpretative understanding of the linguistic attributes that neural fake news generators may most successfully exploit. When comparing models trained on subsets of our features and confronting the models with increasingly advanced neural fake news, we find that stylistic features may be the most robust. We discuss our findings, subsequent analyses, and broader implications in the pages within.
Anthology ID:
2022.coling-1.573
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:
6586–6599
Language:
URL:
https://aclanthology.org/2022.coling-1.573
DOI:
Bibkey:
Cite (ACL):
Ankit Aich, Souvik Bhattacharya, and Natalie Parde. 2022. Demystifying Neural Fake News via Linguistic Feature-Based Interpretation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6586–6599, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Demystifying Neural Fake News via Linguistic Feature-Based Interpretation (Aich et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.573.pdf
Data
RealNews