Practices related to health are circulated widely on YouTube. With a health psychology perspective, we present a study to understand health and wellbeing-related practices of a group of popular, professional YouTubers from the audio-visual content they produce. We first identify, via polytextual thematic analysis, six thematic healthrelated categories, and use them to label a set of 2500 YouTube videos. Agreement among three independent annotators was acceptable for these health-related categories. We then present an analysis of speech transcriptions and visual content, demonstrating that distinctive patterns exist for these health-related categories. These include linguistic markers and specific scene types and objects. Finally, with an interpretability focus, we study the feasibility of classifying health-related video categories in a binary setting, and compare performance across features, finding best accuracy for linguistic features (74-87%), and various patterns of linguistic and visual relevance used for the classification of health categories. The results shows promise to support mixed-methods research in health psychology, combining manual analysis and data-driven methods. More generally, our work contributes to the understanding of current health practices shared and promoted on social video.
CCS Concepts• Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile computing design and evaluation methods.