[2603.22977] DariMis: Harm-Aware Modeling for Dari Misinformation Detection on YouTube

Computer Science > Computation and Language arXiv:2603.22977 (cs) [Submitted on 24 Mar 2026] Title:DariMis: Harm-Aware Modeling for Dari Misinformation Detection on YouTube Authors:Jawid Ahmad Baktash, Mosa Ebrahimi, Mohammad Zarif Joya, Mursal Dawodi View a PDF of the paper titled DariMis: Harm-Aware Modeling for Dari Misinformation Detection on YouTube, by Jawid Ahmad Baktash and 3 other authors View PDF HTML (experimental) Abstract:Dari, the primary language of Afghanistan, is spoken by tens of millions of people yet remains largely absent from the misinformation detection literature. We address this gap with DariMis, the first manually annotated dataset of 9,224 Dari-language YouTube videos, labeled across two dimensions: Information Type (Misinformation, Partly True, True) and Harm Level (Low, Medium, High). A central empirical finding is that these dimensions are structurally coupled, not independent: 55.9 percent of Misinformation carries at least Medium harm potential, compared with only 1.0 percent of True content. This enables Information Type classifiers to function as implicit harm-triage filters in content moderation pipelines. We further propose a pair-input encoding strategy that represents the video title and description as separate BERT segment inputs, explicitly modeling the semantic relationship between headline claims and body content, a key signal of misleading information. An ablation study against single-field concatenation shows that pair-input enco...