Mittenentzwei, SarahMurad, DanishPreim, BernhardMeuschke, MoniqueGarrison, LauraJönsson, Daniel2024-09-172024-09-172024978-3-03868-244-82070-5786https://doi.org/10.2312/vcbm.20241193https://diglib.eg.org/handle/10.2312/vcbm20241193The general public is highly interested in medical information, particularly educational media about diseases, healthy biological processes such as pregnancy, and surgical procedures. Efforts to develop educational materials using data-driven approaches like narrative visualization exist, but studies are often performed in lab settings. Since there are few public sources for visualizations of medical image data, YouTube videos, which often contain 3D medical visualizations, are an important reference. We aim to better understand the user base of these videos. Therefore, we curated a dataset of 76 videos featuring medical 3D visualizations. We analyzed 14,550 comments across all videos using manual review and machine learning techniques, including natural language processing for sentiment and emotion analysis of user comments. While few comments directly link visual attributes or design choices to user sentiment, insights into users' motivation and opinions of specific design choices have emerged.Attribution 4.0 International LicenseCCS Concepts: Applied computing → Life and medical sciences; Information systems → Web miningApplied computing → Life and medical sciencesInformation systems → Web mining10.2312/vcbm.202411935 pages