Detection of Human and Machine-Authored Fake News in Urdu
This addresses the spread of misinformation in Urdu, exacerbated by LLMs, but is incremental as it builds on existing detection methods.
The paper tackles the problem of detecting both human-authored and machine-generated fake news in Urdu, a low-resource language, by updating the detection schema and proposing a hierarchical strategy, with experiments demonstrating effectiveness across four datasets.
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.