HIV messaging on Twitter

an analysis of current practice and data-driven recommendations

Sophie Lohmann, Benjamin X. White, Zhen Zuo, Man Pui Sally Chan, Alex Morales, Bo Li, Chengxiang Zhai, Dolores Albarracin

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)2799-2805
Number of pages7
JournalAIDS (London, England)
Volume32
Issue number18
DOIs
StatePublished - Nov 28 2018

Fingerprint

Social Media
HIV
Fear
Statistical Models
Centers for Disease Control and Prevention (U.S.)
Health Promotion
Language
Guidelines

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology
  • Infectious Diseases

Cite this

HIV messaging on Twitter : an analysis of current practice and data-driven recommendations. / Lohmann, Sophie; White, Benjamin X.; Zuo, Zhen; Chan, Man Pui Sally; Morales, Alex; Li, Bo; Zhai, Chengxiang; Albarracin, Dolores.

In: AIDS (London, England), Vol. 32, No. 18, 28.11.2018, p. 2799-2805.

Research output: Contribution to journalArticle

Lohmann, Sophie ; White, Benjamin X. ; Zuo, Zhen ; Chan, Man Pui Sally ; Morales, Alex ; Li, Bo ; Zhai, Chengxiang ; Albarracin, Dolores. / HIV messaging on Twitter : an analysis of current practice and data-driven recommendations. In: AIDS (London, England). 2018 ; Vol. 32, No. 18. pp. 2799-2805.
@article{03149c09392c4983bdd605507331529d,
title = "HIV messaging on Twitter: an analysis of current practice and data-driven recommendations",
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.",
author = "Sophie Lohmann and White, {Benjamin X.} and Zhen Zuo and Chan, {Man Pui Sally} and Alex Morales and Bo Li and Chengxiang Zhai and Dolores Albarracin",
year = "2018",
month = "11",
day = "28",
doi = "10.1097/QAD.0000000000002018",
language = "English (US)",
volume = "32",
pages = "2799--2805",
journal = "AIDS",
issn = "0269-9370",
publisher = "Lippincott Williams and Wilkins",
number = "18",

}

TY - JOUR

T1 - HIV messaging on Twitter

T2 - an analysis of current practice and data-driven recommendations

AU - Lohmann, Sophie

AU - White, Benjamin X.

AU - Zuo, Zhen

AU - Chan, Man Pui Sally

AU - Morales, Alex

AU - Li, Bo

AU - Zhai, Chengxiang

AU - Albarracin, Dolores

PY - 2018/11/28

Y1 - 2018/11/28

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85056419635&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056419635&partnerID=8YFLogxK

U2 - 10.1097/QAD.0000000000002018

DO - 10.1097/QAD.0000000000002018

M3 - Article

VL - 32

SP - 2799

EP - 2805

JO - AIDS

JF - AIDS

SN - 0269-9370

IS - 18

ER -