Measuring the impact of candidates’ tweets on their electoral results

Kartsounidou, Evangelia; Papaxanthi, Dimitra; Andreadis, Ioannis

doi:10.1080/19331681.2022.2069181

Cited by 1 publication

(1 citation statement)

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“…Tweetbotornot2 is among the most cited bot detection tools for health‐related information and allows for bulk processing of large data sets. 19 , 20 , 21 , 22 , 23 …”

Section: Methodsmentioning

confidence: 99%

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

Slavin,

Berman,

Beam

et al. 2024

JAHA

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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.

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“…Tweetbotornot2 is among the most cited bot detection tools for health‐related information and allows for bulk processing of large data sets. 19 , 20 , 21 , 22 , 23 …”

Section: Methodsmentioning

confidence: 99%