Observational studyJournal of medical Internet research2026
Exploring Conversational Dynamics in Scientific and Pseudoscientific Health Communities on YouTube: Process Mining and Network Analysis Study.
Observational study in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 and comparing structural and emotional patterns of conversational flow within a retrieved corpus of YouTube comments from videos about scientific or pseudoscientific health treatments. Methods: We conducted an exploratory observational study using publicly available YouTube comment threads posted between 2011 and 2025 from videos categorized as either "scientific" (20,387 comments) or "pseudoscientific" (32,025 comments) using an automated pipeline that combined API-based data extraction, large language model-based video classification, and natural language processing techniques for multilingual sentiment and thematic classification of comments. We then applied process mining to model the temporal sequences of interactions, and network analysis to map the relationships between conversational topics. Results: Network analysis revealed divergent conversational cores. In the scientific corpus, negative expressions of feelings and negative comparisons had greater normalized node strength, while medical treatment and advice requests were also relatively more prominent. In the pseudoscientific corpus, positive expressions of feelings, thanking, compliments, and emoji-only or brief acknowledgments showed greater prominence. Process mining showed a more heterogeneous combination of negative, positive, and neutral activities in the scientific corpus, whereas the most frequent pathways in the pseudoscientific corpus were concentrated around positive expressions of feelings, thanking, compliments, and brief messages of acknowledgment, or simply emojis. Conclusions: The application of network analysis and process mining techniques revealed distinct patterns in the conversational dynamics of health-related YouTube discussions. Within the sampled corpora, scientific discussions appeared more compatible with mixed-valence evaluation and treatment-related exchanges, whereas pseudoscientific discussions showed patterns more consistent with interpersonal affirmation and socioemotional bonding. These preliminary findings suggest that network analysis and process mining are promising approaches for investigating online health communication and misinformation ecosystems. They also suggest that public health strategies may benefit from considering the affective and community-bonding dimensions of engagement in misinformation communities alongside information provision.
Indexed as
Identifiers
42826380What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.