aceExploring Conversational Dynamics in Scientific and Pseudoscientific Health Communities on YouTube: Process Mining and Network Analysis Study
Background Social media platforms, particularly YouTube (Google LLC), are important sources of health information, but also significant vectors for misinformation and pseudoscience. While many studies analyze the content and sentiment of this information, the dynamic, sequential nature of user interactions, which can shape belief formation and community dynamics, remains poorly understood. Objective This study aimed to explore the applicability of network analysis and process mining techniques for identifying an…
Applying network analysis and process mining to 52,412 YouTube comments distinguished scientific from pseudoscientific health discussions, showing mixed-valence evaluation in scientific threads versus affirmation bonding in pseudoscientific threads.
YouTube health videos serve as significant vectors for misinformation and pseudoscience, with sequential user interactions that can shape belief formation and community dynamics.
Exploratory observational design based on sampled corpora with preliminary findings, limiting generalizability beyond the retrieved YouTube comment threads.
Evidence
- Peer-reviewedJournal of Medical Internet Research2026-10-02
How should this claim be treated?
Truvace Impact Record TRV-2026-1277, v1: “Exploring Conversational Dynamics in Scientific and Pseudoscientific Health Communities on YouTube: Process Mining and Network Analysis Study.” Truvace, 2026-10-04. /record/TRV-2026-1277 (accessed at citation time). sha256 9dfa75d2a2cc1c7d…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1277 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.