ArticleJournal of the American Heart Association2024
Statin Twitter: Human and Automated Bot Contributions, 2010 to 2022.
Article in Journal of the American Heart Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Tracing the Pen: Electronic Health Records Amid the Rise of Generative AI.NPJ digital medicine · 2026Review
- Inaccurate information regarding cardiovascular disease prevention enabled by generative artificial intelligence.American journal of preventive cardiology · 2026Article
- The impact of health misinformation and health literacy on the management of dyslipidemia.American journal of preventive cardiology · 2025Article
- Unveiling Social Media Content Related to ADHD Treatment: Machine Learning Study Using X's Posts over 15 Years.Healthcare (Basel, Switzerland) · 2025Article
- Public perceptions and emotional trends of psychotherapy: a 15-year analysis across English and Spanish language communities on X (twitter).Frontiers in digital health · 2025Article
- Response to Letter to the Editor, "Is ChatGPT an Accurate and Reliable Source of Information for Patients with Vaccine and Statin Hesitancy?"Medeniyet medical journal · 2024Article
- Social Media Posts on Statins: What Can We Learn About Patient Experiences and Perspectives?Journal of the American Heart Association · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundMany individuals eligible for statin therapy decline treatment, often due to fear of adverse effects. Misinformation about statins is common and drives statin reluctance, but its prevalence on social media platforms, such as Twitter (now X) remains unclear. Social media bots are known to proliferate medical misinformation, but their involvement in statin-related discourse is unknown. This study examined temporal trends in volume, author type (bot or human), and sentiment of statin-related Twitter posts (tweets). METHODS AND
resultsWe analyzed original tweets with statin-related terms from 2010 to 2022 using a machine learning-derived classifier to determine the author's bot probability, natural language processing to assign each tweet a negative or positive sentiment, and manual qualitative analysis to identify statin skepticism in a random sample of all tweets and in highly influential tweets. We identified 1 155 735 original statin-related tweets. Bots produced 333 689 (28.9%), humans produced 699 876 (60.6%), and intermediate probability accounts produced 104 966 (9.1%). Over time, the proportion of bot tweets decreased from 47.8% to 11.3%, and human tweets increased from 43.6% to 79.8%. The proportion of negative-sentiment tweets increased from 27.8% to 43.4% for bots and 30.9% to 38.4% for humans. Manually coded statin skepticism increased from 8.0% to 19.0% for bots and from 26.0% to 40.0% for humans.
conclusionsOver the past decade, humans have overtaken bots as generators of statin-related content on Twitter. Negative sentiment and statin skepticism have increased across all user types. Twitter may be an important forum to combat statin-related misinformation.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.