HIV messaging on Twitter: an analysis of current practice and data-driven recommendations.

Abstract

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. 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. 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). 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.