Go to ACL ARR 2024 December homepageDetection of Human and Machine-Authored Fake News in Urdu
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 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 with a 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. We release our collected datasets and code in URL withheld.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: rumor/misinformation detection, fact checking
Contribution Types: NLP engineering experiment, Approaches to low-resource settings
Languages Studied: Urdu
Submission Number: 2016
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