Detection of Human and Machine-Authored Fake News in Urdu

The rise of social media has amplified the spread of fake news, now further complicated by large language models (LLMs) like Chat-GPT, 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. 1

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.acl-long.170
Citations
1
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
4.20
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article

Detection of Human and Machine-Authored Fake News in Urdu

Y. Wang, Mohamad Wijayanuddin Ali, Bernhard Pfahringer, Tony Smith
1 citations
Misinformation and Its Impacts
4.20
article

Detection of Human and Machine-Authored Fake News in Urdu

Y. Wang, Mohamad Wijayanuddin Ali, Bernhard Pfahringer, Tony Smith
article en
1 citations

Abstract

The rise of social media has amplified the spread of fake news, now further complicated by large language models (LLMs) like Chat-GPT, 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. 1

Gender equality
Openalex Percentile: Top 6%
Misinformation and Its Impacts
4.20
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