Evidence map›Paper›PMID 38533942›Full record

ArticleJournal of the American Heart Association2024

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

Samuel D Slavin, Adam N Berman, Andrew L Beam, Ann Marie Navar, Murray A Mittleman

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Samuel D SlavinBrigham and Women's Hospital Boston MA USA.ORCID 0000-0002-7208-1351
Adam N BermanBrigham and Women's Hospital Boston MA USA.ORCID 0000-0002-0724-9779
Andrew L BeamHarvard T.H. Chan School of Public Health Boston MA USA.ORCID 0000-0002-6657-2787
Ann Marie NavarUT Southwestern Medical Center Dallas TX USA.ORCID 0000-0002-6197-9860
Murray A MittlemanHarvard T.H. Chan School of Public Health Boston MA USA.ORCID 0000-0001-9788-7274

Funding

TRAINING PROGRAM IN CARDIOVASCULAR RESEARCHT32HL007604 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MARK W FEINBERG · 1985 to 2026
$19.0M
NHLBI NIH HHS T32 HL007604
6 · The paper itself

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.

Indexed as

Hydroxymethylglutaryl-CoA Reductase InhibitorsSocial MediaCommunicationHumansNatural Language ProcessingSoftwareHydroxymethylglutaryl-CoA Reductase Inhibitorscardiovascular preventionmisinformationsocial mediastatins

Identifiers

PMID38533942
PMCPMC11179764

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.