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INTRODUCTION: Despite the potential of social media to influence public health and generate insights, the process of monitoring and analyzing the dissemination of health care messages on social media has been described as difficult and in need of improvement. OBJECTIVES: The objective of this study was to describe and demonstrate a reproducible methodology for cataloging and analyzing health care-related social media comments and provide insight into how clinicians and members of the general public respond to health care messaging on social media. METHODS: , FiveThirtyEight, and Dentaltown. Using data provided in the comments, we conducted engagement analysis as well as content, network, and sentiment analysis across 8 categories. RESULTS: We collected 671 comments. Among our findings, Facebook (472 of 671) was the most popular platform among commentators; almost half of all comments (335 of 671) aligned with the recommendations of the 2016 American Dental Association sealants guideline; clinicians were more likely than the general public to like a comment that suggested an improvement to the guideline; and >75% of comments (521 of 671) were supported by anecdotal evidence. CONCLUSION: As the prevalence of anecdotes on social media suggests, the likelihood of falsehoods spreading on social media is high. Insights gleaned from the methodology described in this research could help combat the spread of such misinformation by providing disseminators of health care messaging with insight into their target audiences. Armed with this knowledge, disseminators can craft health care messages that more effectively engage clinicians and the general public. KNOWLEDGE TRANSFER STATEMENT: The methodology used in this research provides a reproducible strategy for tracking social media engagement with health care messages. Engagement results can assist future delivery of health care messages to key stakeholders and ensure better implementation and adoption of these communications.
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Stored only when both papers are in the corpus — 19 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.