Detection of Human and Machine-Authored Fake News in Urdu
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909365135474688 |
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| author | Ali, Muhammad Zain Wang, Yuxia Pfahringer, Bernhard Smith, Tony |
| author_facet | Ali, Muhammad Zain Wang, Yuxia Pfahringer, Bernhard Smith, Tony |
| contents | 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 also becomes 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 detection schema to include machine-generated news with focus on the Urdu language. We further propose a hierarchical detection strategy to improve the accuracy and robustness. Experiments show its effectiveness across four datasets in various settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19517 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detection of Human and Machine-Authored Fake News in Urdu Ali, Muhammad Zain Wang, Yuxia Pfahringer, Bernhard Smith, Tony Computation and Language Machine Learning 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 also becomes 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 detection schema to include machine-generated news with focus on the Urdu language. We further propose a hierarchical detection strategy to improve the accuracy and robustness. Experiments show its effectiveness across four datasets in various settings. |
| title | Detection of Human and Machine-Authored Fake News in Urdu |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.19517 |