Artificial Intelligence–Enabled Analysis of Statin-Related Topics and Sentiments on Social Media

Abstract

This qualitative study evaluates user-generated, publicly available posts and comments about statin-related topics: ketogenic diets, diabetes, and supplements; statin adverse effects; statin hesitancy; clinical trial appraisals; pharmaceutical industry bias; and red yeast rice.

Key Points

Question

What are public perceptions of statins as expressed on a widely used social media platform?

Findings

In this qualitative study of 10 233 unique statin-related discussions, an artificial intelligence (AI) pipeline was developed to analyze these discussions, which were automatically categorized into 100 topics and 6 thematic groups (ketogenic diets, diabetes, supplements, and statins; statin adverse effects; statin hesitancy; clinical trial appraisals; pharmaceutical industry bias and statins; and red yeast rice and statins). Sentiment analysis of these discussions showed that most of them had a neutral or negative sentiment.

Meaning

Findings from this study suggest that AI-enabled analysis of large, contemporary social media data may generate insights into public perceptions and help guide strategies for addressing barriers to statin use and adherence.

Abstract

Importance

Despite compelling evidence that statins are safe, are generally well tolerated, and reduce cardiovascular events, statins are underused even in patients with the highest risk. Social media may provide contemporary insights into public perceptions about statins.

Objective

To characterize and classify public perceptions about statins that were gleaned from more than a decade of statin-related discussions on Reddit, a widely used social media platform.

Design, Setting, and Participants

This qualitative study analyzed all statin-related discussions on the social media platform that were dated between January 1, 2009, and July 12, 2022. Statin- and cholesterol-focused communities, were identified to create a list of statin-related discussions. An artificial intelligence (AI) pipeline was developed to cluster these discussions into specific topics and overarching thematic groups. The pipeline consisted of a semisupervised natural language processing model (BERT [Bidirectional Encoder Representations from Transformers]), a dimensionality reduction technique, and a clustering algorithm. The sentiment for each discussion was labeled as positive, neutral, or negative using a pretrained BERT model.

Exposures

Statin-related posts and comments containing the terms statin and cholesterol.

Main Outcomes and Measures

Statin-related topics and thematic groups.

Results

A total of 10 233 unique statin-related discussions (961 posts and 9272 comments) from 5188 unique authors were identified. The number of statin-related discussions increased by a mean (SD) of 32.9% (41.1%) per year. A total of 100 discussion topics were identified and were classified into 6 overarching thematic groups: (1) ketogenic diets, diabetes, supplements, and statins; (2) statin adverse effects; (3) statin hesitancy; (4) clinical trial appraisals; (5) pharmaceutical industry bias and statins; and (6) red yeast rice and statins. The sentiment analysis revealed that most discussions had a neutral (66.6%) or negative (30.8%) sentiment.

Conclusions and Relevance

Results of this study demonstrated the potential of an AI approach to analyze large, contemporary, publicly available social media data and generate insights into public perceptions about statins. This information may help guide strategies for addressing barriers to statin use and adherence.