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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- 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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.573.pdf
- Data
- RealNews
Export citation
@inproceedings{aich-etal-2022-demystifying,
title = "Demystifying Neural Fake News via Linguistic Feature-Based Interpretation",
author = "Aich, Ankit and
Bhattacharya, Souvik and
Parde, Natalie",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.573",
pages = "6586--6599",
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.",
}
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%0 Conference Proceedings %T Demystifying Neural Fake News via Linguistic Feature-Based Interpretation %A Aich, Ankit %A Bhattacharya, Souvik %A Parde, Natalie %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F aich-etal-2022-demystifying %X 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. %U https://aclanthology.org/2022.coling-1.573 %P 6586-6599
Markdown (Informal)
[Demystifying Neural Fake News via Linguistic Feature-Based Interpretation](https://aclanthology.org/2022.coling-1.573) (Aich et al., COLING 2022)
- Demystifying Neural Fake News via Linguistic Feature-Based Interpretation (Aich et al., COLING 2022)
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.