{
  "id": 10314,
  "url": "https://arxiv.org/abs/2607.12336",
  "title": "Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)",
  "summary": "arXiv:2607.12336v1 Announce Type: cross Abstract: Artificial Intelligence (AI) technologies, while serving as a foundational enabler for modern social media and digital health services, exert a bivalent effect by simultaneously acting as a combatant against and a spread vector for misinformation. A prevalent challenge in mitigating this issue arises in non-English contexts and low socioeconomic classes, where limited data hinders the training of AI models for effective detection. Consequently, c",
  "authors": "Farnaz Farid, Raihan Alam, Al Al-Areqi, Farhad Ahamed, Muhammad Hassan Khan, Sadia Hossain, Irena Veljanova, Anika Tabassum Binte Hossain",
  "category": "research",
  "topics": "misinformation,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-15T04:00:00.000Z",
  "fetched_at": "2026-07-15T05:10:55.633Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/10314",
  "original_url": "https://arxiv.org/abs/2607.12336",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}