ArticleAIDS (London, England)2018
HIV messaging on Twitter: an analysis of current practice and data-driven recommendations.
Article in AIDS (London, England), 2018. 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
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Who cites it
7 citing papers in PubMed.
- This is fine: Using memes to examine resilience, coping, and community for sexual minority men and nonbinary individuals during COVID-19.American journal of community psychology · 2025Article
- From Virus to Viral: Content Analysis of HIV-related Twitter Messages among Young Men in the U.S.BMC digital health · 2024Article
- Content Analysis of American Network News Coverage of Prevention Strategies During the Initial Wave of the COVID-19 Pandemic.Journal of general internal medicine · 2023Article
- User- and Message-Level Correlates of Endorsement and Engagement for HIV-Related Messages on Twitter: Cross-sectional Study.JMIR public health and surveillance · 2022Article
- Two is better than one: Using a single emotion lexicon can lead to unreliable conclusions.PloS one · 2022Article
- Understanding how young African adults interact with peer-generated sexual health information on Facebook and uncovering strategies for successful organic engagement.BMC public health · 2021Article
- Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy.Human vaccines & immunotherapeutics · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesSocial 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
methodsWe 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.
resultsFear-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).
conclusionsFear 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.
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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.