BOUTEF: New Multilingual Corpus for Studying Fake News in North Africa
Researchers have created BOUTEF, a large-scale multilingual dataset containing fake news, genuine content, and user comments from Algeria and Tunisia across multiple languages and dialects. The corpus includes verified debunking information and covers Arabic dialects, French, English, and code-switched language varieties. The dataset and analysis reveal that fake news relies on emotionally charged narratives and sensational framing, while debunking content is more factual, with implications for improving misinformation detection in under-resourced linguistic contexts.
BOUTEF is a comprehensive multilingual corpus designed to study fake news propagation in North Africa, addressing a gap in research for under-resourced languages and regions. The dataset integrates three components: fake narratives, genuine narratives, user-generated comments, and debunking information, covering Modern Standard Arabic (MSA), Algerian and Tunisian dialects, Arabizi, French, English, and code-switched language. Through quantitative and qualitative analysis, researchers found significant associations between thematic categories and message veracity, with fake news characterized by emotionally charged language, sensational framing, and hybrid linguistic practices that increase virality. Debunking content, by contrast, adopts a more factual and verification-oriented style. The comparative analysis between Algeria and Tunisia reveals both shared misinformation dynamics and country-specific patterns shaped by local sociopolitical contexts, emphasizing the role of informal language practices in misinformation diffusion.
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- arXiv cs.CLCenter
BOUTEF: A Multilingual Corpus for FakeNews in North Africa -- Language as a Weapon
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