Message Signals and Peer Response in an Atrial Fibrillation Online Health Community: Observational Study of Reddit Posts Using Large Language Model Annotation (Preprint)

BACKGROUND Online health communities enable patients to seek information, share experiences, and obtain peer support. Yet some messages prompt extensive discussion while others receive little response. Drawing on signaling theory and social-support research, this study examines how readers use message features to infer a poster’s situation and decide whether and how to respond. OBJECTIVE This study investigated how medical terminology density and emotional intensity, separately and jointly, relate to the volume and elaboration of peer responses in an online atrial fibrillation (Afib) community. METHODS This study analyzed 5028 posts and 71,592 comments from the r/AFIB subreddit, posted between February 2023 and November 2024 and collected using the Python Reddit API Wrapper. The analytic sample included only posts that received at least one comment. Discussion topics were identified using BERTopic. A large language model annotated post intention, topic, emotional intensity, disease outlook, and coping confidence. The label definitions were refined with an Afib domain expert and validated against a three-coder majority vote. Finally, medical terminology density was calculated using a curated Afib glossary. Drawing on signaling theory, medical terminology density was treated as a competence-threshold signal and emotional intensity as a need signal. Negative binomial regression estimated comment counts, and linear regression estimated mean comment length and comment richness. Four models were estimated for each outcome. RESULTS A higher density of medical terminology was associated with fewer comments (B=-0.494, 95% CI [-0.982, -0.006], incidence rate ratio [IRR]=0.610, P=.047), whereas emotional intensity was associated with more comments (B=0.007, 95% CI[0.005, 0.009], IRR=1.007, P<.001). Their positive interaction (B=0.029, 95% CI[0.009,0.049], IRR=1.029, P=.004) indicated that the negative association between technical language and response volume was weaker for more emotional messages. Medical terminology was not significantly associated with comment length or comment richness. Messages requesting peer experience (IRR=1.147, P<.001) and, as an exploratory finding, messages seeking or offering psychological support (IRR=1.088, P=.009) received more replies, while on the other hand messages asking for medical advice (IRR=0.886, P<.001) and messages about family (IRR=0.860, P<.001) received fewer. CONCLUSIONS In online Afib communities, message features appear to function as signals that shape peer engagement. Medical terminology may limit participation by signaling a higher expertise requirement, whereas emotional expression may broaden participation by signaling a need for support. Response volume and response elaboration showed different patterns, indicating that engagement should not be treated as a single construct. These findings offer guidance for platform moderators and health communicators seeking to enhance accessible peer support.