HIV messaging on Twitter: an analysis of current practice and data-driven recommendations.
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ABSTRACT: Objectives
Social media messages have been increasingly used in health campaigns about prevention, testing, and treatment of HIV. We identified factors leading to the retransmission of messages from expert social media accounts to create data-driven recommendations for online HIV messaging.Design and methods
We sampled 20 201 HIV-related tweets (posted between 2010 and 2017) from 37 HIV experts. Potential predictors of retransmission were identified based on prior literature and machine learning methods, and were subsequently analyzed using multilevel negative binomial models.Results
Fear-related language, longer messages, and including images (e.g. photos, gif, or videos) were the strongest predictors of retweet counts. These findings were similar for messages authored by HIV experts, and also messages retransmitted by experts, but created by nonexperts (e.g. celebrities or politicians).Conclusions
Fear appeals affect how much HIV messages spread on Twitter, as do structural characteristics, like the length of the tweet and inclusion of images. A set of five data-driven recommendations for increasing message spread is derived and discussed in the context of current centers for disease control and prevention social media guidelines.
SUBMITTER: Lohmann S
PROVIDER: S-EPMC6615455 | biostudies-literature | 2018 Nov
REPOSITORIES: biostudies-literature
Publications
HIV messaging on Twitter: an analysis of current practice and data-driven recommendations.
Lohmann Sophie S White Benjamin X BX Zuo Zhen Z Chan Man-Pui Sally MS Morales Alex A Li Bo B Zhai Chengxiang C Albarracín Dolores D
AIDS (London, England) 20181101 18
<h4>Objectives</h4>Social media messages have been increasingly used in health campaigns about prevention, testing, and treatment of HIV. We identified factors leading to the retransmission of messages from expert social media accounts to create data-driven recommendations for online HIV messaging.<h4>Design and methods</h4>We sampled 20 201 HIV-related tweets (posted between 2010 and 2017) from 37 HIV experts. Potential predictors of retransmission were identified based on prior literature and ...[more]
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