Statin Twitter: Human and Automated Bot Contributions, 2010 to 2022

Abstract

Background Many 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 Results We 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. Conclusions Over 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.

Social Media Posts on Statins: What Can We Learn About Patient Experiences and Perspectives?

“…The study by Slavin et al 18 in this issue of the Journal of the American Heart Association (JAHA) analyzed over a million statin‐related original tweets over a 12‐year period and revealed several concerning trends, including the increasing prevalence of negative sentiment and statin skepticism. These trends were found across tweets from both bots and humans.…”

“…For researchers interested in using Twitter data to study patients' experiences with statins, the findings of Slavin et al 18 also highlight the need to automatically distinguish between tweets that discuss statins and tweets posted by those actually taking statins. 19 Although this distinction is not necessary in platforms such as WebMD, where users are understood to be taking the medication they are discussing, the study by Slavin et al 18 suggests that many people who are discussing statins on X/Twitter have not actually used them.…”

“…For researchers interested in using Twitter data to study patients' experiences with statins, the findings of Slavin et al 18 also highlight the need to automatically distinguish between tweets that discuss statins and tweets posted by those actually taking statins. 19 Although this distinction is not necessary in platforms such as WebMD, where users are understood to be taking the medication they are discussing, the study by Slavin et al 18 suggests that many people who are discussing statins on X/Twitter have not actually used them. Similar evidence of this can be seen in our own work, in which we found that only 44% of tweets that mentioned specific statins actually discussed health (eg, as opposed to finances, sales, or jokes), and only one‐third of that 44% evidenced personal use of statins.…”

“…For example, we found only 4% of users who reported their pregnancy, 21 that is, personal information, on Twitter were nonpersonal accounts. We hypothesize that including simple keywords and patterns (such as I or my) that indicate a personal report to the set of statin‐related words used for collection could filter out a great proportion of the bots found by Slavin et al, 18 if the focus of the study were on personal reports and analyzing the content of the posts by humans, which has been the focus of our own studies.…”

“…One interesting angle of the study by Slavin et al 18 that deserves further treatment is that of the trends observed in the spread of misinformation (factually inaccurate information). The diffusion of misinformation can often be motivated by uncertainty, whereby a user seeks to elicit confirmatory or contradictory opinions from other users in the face of uncertainty about the benefits and adverse effects of statins or believes what is stated and seeks to warn others.…”