@inproceedings{ali-etal-2025-detection,
title = "Detection of Human and Machine-Authored Fake News in {U}rdu",
author = "Ali, Muhammad Zain and
Wang, Yuxia and
Pfahringer, Bernhard and
Smith, Tony C",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.170/",
doi = "10.18653/v1/2025.acl-long.170",
pages = "3419--3428",
ISBN = "979-8-89176-251-0",
abstract = "The rise of social media has amplified the spread of fake news, now further complicated by large language models (LLMs) like ChatGPT, which ease the generation of highly convincing, error-free misinformation, making it increasingly challenging for the public to discern truth from falsehood. Traditional fake news detection methods relying on linguistic cues have also become less effective. Moreover, current detectors primarily focus on binary classification and English texts, often overlooking the distinction between machine-generated true vs. fake news and the detection in low-resource languages. To this end, we updated the detection schema to include machine-generated news focusing on Urdu. We further propose a conjoint detection strategy to improve the accuracy and robustness. Experiments show its effectiveness across four datasets in various settings."
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<abstract>The rise of social media has amplified the spread of fake news, now further complicated by large language models (LLMs) like ChatGPT, which ease the generation of highly convincing, error-free misinformation, making it increasingly challenging for the public to discern truth from falsehood. Traditional fake news detection methods relying on linguistic cues have also become less effective. Moreover, current detectors primarily focus on binary classification and English texts, often overlooking the distinction between machine-generated true vs. fake news and the detection in low-resource languages. To this end, we updated the detection schema to include machine-generated news focusing on Urdu. We further propose a conjoint detection strategy to improve the accuracy and robustness. Experiments show its effectiveness across four datasets in various settings.</abstract>
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%0 Conference Proceedings
%T Detection of Human and Machine-Authored Fake News in Urdu
%A Ali, Muhammad Zain
%A Wang, Yuxia
%A Pfahringer, Bernhard
%A Smith, Tony C.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F ali-etal-2025-detection
%X The rise of social media has amplified the spread of fake news, now further complicated by large language models (LLMs) like ChatGPT, which ease the generation of highly convincing, error-free misinformation, making it increasingly challenging for the public to discern truth from falsehood. Traditional fake news detection methods relying on linguistic cues have also become less effective. Moreover, current detectors primarily focus on binary classification and English texts, often overlooking the distinction between machine-generated true vs. fake news and the detection in low-resource languages. To this end, we updated the detection schema to include machine-generated news focusing on Urdu. We further propose a conjoint detection strategy to improve the accuracy and robustness. Experiments show its effectiveness across four datasets in various settings.
%R 10.18653/v1/2025.acl-long.170
%U https://aclanthology.org/2025.acl-long.170/
%U https://doi.org/10.18653/v1/2025.acl-long.170
%P 3419-3428
Markdown (Informal)
[Detection of Human and Machine-Authored Fake News in Urdu](https://aclanthology.org/2025.acl-long.170/) (Ali et al., ACL 2025)
ACL
- Muhammad Zain Ali, Yuxia Wang, Bernhard Pfahringer, and Tony C Smith. 2025. Detection of Human and Machine-Authored Fake News in Urdu. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3419–3428, Vienna, Austria. Association for Computational Linguistics.