**Background:** Statins conclusively reduce morbidity and mortality from atherosclerotic cardiovascular disease (ASCVD), the leading cause of death worldwide, and represent nearly 17% of all US prescriptions. Despite their established benefits, statin use remains suboptimal in high-risk individuals. Understanding patient-level perspectives on statins is crucial for improving adherence, but traditional surveys and focus groups may lack generalizability. Social media platforms like Reddit (52 million daily active users, ~430 million monthly users) offer large-scale, unsolicited patient-generated data that may reveal novel insights and misinformation affecting statin adherence. Artificial intelligence methods, particularly natural language processing, can facilitate analysis of these vast datasets.
**Methods:** This qualitative study collected publicly available Reddit posts and comments containing the word "statin" or generic/brand names of specific statins (atorvastatin, lipitor, rosuvastatin, crestor, pitavastatin, livalo, zypitamag, simvastatin, zocor, pravastatin, pravachol, lovastatin, altoprev, fluvastatin, lescol) from January 1, 2009, to July 12, 2022. Data were retrieved via the Pushshift API from 19 communities identified by searching for "statin" and "cholesterol." Topic modeling was performed using BERTopic with the all-MiniLM-L6-v2 pretrained model, UMAP dimensionality reduction, and spectral clustering. Clustering performance was assessed using Silhouette coefficient and Davies-Bouldin index. Sentiment analysis was conducted using a pretrained RoBERTa model trained on social media posts, classifying text as positive, neutral, or negative, with scores transformed to −1 (negative), 0 (neutral), and 1 (positive).
**Key Results:** A total of 10,233 unique statin-related discussions were curated (961 posts, 9,272 comments) from 5,188 unique authors. Posts averaged 1,792.9 characters (SD 2,693.5) and comments 839.9 characters (SD 1,122.1). The most common search term was "statin" (74.0%), followed by "Lipitor" (12.7%) and "atorvastatin" (4.2%). The most frequent communities were r/keto (23.1%) and r/Cholesterol (21.3%). Statin-related discussions increased by a mean of 32.9% (SD 41.1%) per year. Topic modeling identified 100 topics (Silhouette coefficient 0.013, Davies-Bouldin index 4.27), which were organized into 6 overarching groups: (1) ketogenic diets, diabetes, supplements, and statins (691 posts, 3,497 comments); (2) statin adverse effects (102 posts, 1,768 comments); (3) statin hesitancy (79 posts, 1,775 comments); (4) clinical trial appraisals (73 posts, 1,271 comments); (5) pharmaceutical industry bias and statins (10 posts, 837 comments); and (6) red yeast rice and statins (6 posts, 124 comments). Sentiment analysis revealed 30.8% negative, 66.6% neutral, and 2.6% positive discussions, with a mean sentiment score of −0.28 (SD 0.50). No group had positive sentiment. The most negative sentiment was in r/conspiracy (−0.52, SD 0.51) and the most neutral in r/COVID19 (−0.12, SD 0.35).
**Clinical Implications:** This study demonstrates that AI can automate the extraction and analysis of social media data to understand public perceptions about statins, complementing prior manual analyses. The identified themes align with known barriers to statin adherence from studies like USAGE and PALM registries, including adverse effect concerns (myalgias, diabetes risk, cognitive dysfunction), disbelief in the LDL-C hypothesis, preference for lifestyle alternatives, and health care disenfranchisement. Novel topics emerged, including statins' role in COVID-19 outcomes, ketogenic diet-related dyslipidemia, coronary artery calcium scoring for statin decisions, and concerns about fetal stem cell use in statin development. The predominantly negative sentiment and identified misinformation (e.g., distrust of LDL-C causality, unfounded safety concerns) are concerning, as prior Danish research showed unfavorable media coverage decreased new statin use and increased discontinuation within one year. These findings underscore the need for active public health efforts to monitor and address health misinformation on accessible social media platforms, particularly as Reddit content is freely visible and highly ranked in search engines.