Abstract
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.