Large Language Model-Based Analysis of Statin Therapy Discussions and Sentiment on Social Media: Cross-Sectional Observational Study

Clinical Snapshot

Key Findings

• P Value: Not applicable (descriptive study)

• Effect Size: Negative sentiment in 25.2% of statin-directed discussions, neutral in 44.1%, positive in 12.5%

• Primary Outcome: Sentiment analysis of statin-related discussions

• Nnt Or Sensitivity: Adverse effects mentioned in 31.9% of discussions; adherence issues in 29.8%

• Confidence Interval: Not reported

Clinical Application

Insights applicable to clinical communication strategies, though direct clinical application limited Relevant to PBS-subsidised statin prescribing patterns; may inform TGA pharmacovigilance and RACGP patient communication guidelines Patients prescribed or considering statin therapy, particularly those seeking online health information

Abstract

BACKGROUND: Statin therapy, despite proven cardiovascular benefits, remains underused. Social media platforms may capture patient perspectives that are less visible in clinical encounters. OBJECTIVE: This study aimed to characterize themes, sentiment, and decision-making factors related to statin therapy through large language model (LLM)-based analysis of Reddit discussions. METHODS: This cross-sectional observational study analyzed English-language Reddit posts and comments mentioning statins from January 2022 to May 2025, identified via keyword-based Reddit application programming interface searches (≤1000 posts per keyword). A total of 5328 retrieved discussions (n=1661, 31.2% posts and n=3667, 68.8% keyword-containing comments) from public subreddits were included. Themes, sentiments (positive, neutral, or negative), guideline-informed clinical relevance, information-seeking behavior, adverse effect mentions, decision factors, and adherence-related content were extracted using an LLM-based pipeline. RESULTS: Among 5328 discussions, prominent topics included adverse effects (n=1697, 31.9%), decision-making references related to laboratory results and physician advice (n=2767, 51.9% and n=2034, 38.2%, respectively), and alternative approaches (n=2485, 46.6%). Overall sentiment was neutral in 34% (n=1812) of discussions, negative in 30.9% (n=1646), and positive in 16.9% (n=900); the remainder were mixed or unclear. Statin-directed sentiment was neutral in 44.1% (n=2350) of discussions, negative in 25.2% (n=1343), and positive in 12.5% (n=666); the remainder did not express statin-directed sentiment. High clinical relevance was identified in 12.6% (n=672) of discussions. Adherence-related issues were mentioned in 29.8% (n=1587) of discussions. Among adverse effect mentions, muscle pain (n=129, 7.6%) and fatigue (n=110, 6.5%) were common. CONCLUSIONS: LLM-enabled analysis of Reddit discourse highlights substantial negative sentiment, adherence-related concerns, and adverse effect narratives surrounding statin therapy. These findings suggest opportunities for patient-centered communication and shared decision-making strategies that address symptom attribution, uncertainty, and information needs in digital information environments.

References

1. 1.Liu, S., & Liu, J. (2026). Large Language Model-Based Analysis of Statin Therapy Discussions and Sentiment on Social Media: Cross-Sectional Observational Study. Journal of Medical Internet Research, 28(4), e85057. https://doi.org/10.2196/85057