{
  "id": 18487,
  "url": "https://www.jmir.org/2026/1/e73364",
  "title": "A Machine Learning Pipeline to Analyze Global Sentiment and Factors Influencing Retinoblastoma Treatment Hesitancy: Observational Infodemiology Study",
  "summary": "Background: The use of social media in cancer research, patient support, and information sharing has been well documented. Objective: Using retinoblastoma as a model, we use the information provided from Twitter (subsequently rebranded X) to understand patients’ treatment-seeking behavior and barriers, as well as investigate its application in research and epidemiology for rare diseases. Methods: Posts on retinoblastoma were extracted from Twitter. We trained BERT (Bidirectional Encoder Represen",
  "authors": "Emily S Wong, Richard W Choy, Esther W Tang, Yuzhou Zhang, Xiu Juan Zhang, Linbin Zhou, Wai Kit Chu, Li Jia Chen, Clement C Tham, Chi Pui Pang, Jason C Yam",
  "category": "research",
  "topics": "healthcare,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-11T19:30:15.000Z",
  "fetched_at": "2026-08-12T05:10:43.828Z",
  "source_slug": "x-jmir-journal-of-medical-internet-researc",
  "source_name": "JMIR (Journal of Medical Internet Research)",
  "source_homepage": "https://www.jmir.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18487",
  "original_url": "https://www.jmir.org/2026/1/e73364",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}