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Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study

BACKGROUND: Adolescents and young adults in the age range of 13-24 years are at the highest risk of developing HIV infections. As social media platforms are extremely popular among youths, researchers can utilize these platforms to curb the HIV epidemic by investigating the associations between the...

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Autores principales: Stevens, Robin, Bonett, Stephen, Bannon, Jacqueline, Chittamuru, Deepti, Slaff, Barry, Browne, Safa K, Huang, Sarah, Bauermeister, José A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7380998/
https://www.ncbi.nlm.nih.gov/pubmed/32579119
http://dx.doi.org/10.2196/17196
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author Stevens, Robin
Bonett, Stephen
Bannon, Jacqueline
Chittamuru, Deepti
Slaff, Barry
Browne, Safa K
Huang, Sarah
Bauermeister, José A
author_facet Stevens, Robin
Bonett, Stephen
Bannon, Jacqueline
Chittamuru, Deepti
Slaff, Barry
Browne, Safa K
Huang, Sarah
Bauermeister, José A
author_sort Stevens, Robin
collection PubMed
description BACKGROUND: Adolescents and young adults in the age range of 13-24 years are at the highest risk of developing HIV infections. As social media platforms are extremely popular among youths, researchers can utilize these platforms to curb the HIV epidemic by investigating the associations between the discourses on HIV infections and the epidemiological data of HIV infections. OBJECTIVE: The goal of this study was to examine how Twitter activity among young men is related to the incidence of HIV infection in the population. METHODS: We used integrated human-computer techniques to characterize the HIV-related tweets by male adolescents and young male adults (age range: 13-24 years). We identified tweets related to HIV risk and prevention by using natural language processing (NLP). Our NLP algorithm identified 89.1% (2243/2517) relevant tweets, which were manually coded by expert coders. We coded 1577 HIV-prevention tweets and 17.5% (940/5372) of general sex-related tweets (including emojis, gifs, and images), and we achieved reliability with intraclass correlation at 0.80 or higher on key constructs. Bivariate and multivariate analyses were performed to identify the spatial patterns in posting HIV-related tweets as well as the relationships between the tweets and local HIV infection rates. RESULTS: We analyzed 2517 tweets that were identified as relevant to HIV risk and prevention tags; these tweets were geolocated in 109 counties throughout the United States. After adjusting for region, HIV prevalence, and social disadvantage index, our findings indicated that every 100-tweet increase in HIV-specific tweets per capita from noninstitutional accounts was associated with a multiplicative effect of 0.97 (95% CI [0.94-1.00]; P=.04) on the incidence of HIV infections in the following year in a given county. CONCLUSIONS: Twitter may serve as a proxy of public behavior related to HIV infections, and the association between the number of HIV-related tweets and HIV infection rates further supports the use of social media for HIV disease prevention.
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spelling pubmed-73809982020-08-06 Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study Stevens, Robin Bonett, Stephen Bannon, Jacqueline Chittamuru, Deepti Slaff, Barry Browne, Safa K Huang, Sarah Bauermeister, José A J Med Internet Res Original Paper BACKGROUND: Adolescents and young adults in the age range of 13-24 years are at the highest risk of developing HIV infections. As social media platforms are extremely popular among youths, researchers can utilize these platforms to curb the HIV epidemic by investigating the associations between the discourses on HIV infections and the epidemiological data of HIV infections. OBJECTIVE: The goal of this study was to examine how Twitter activity among young men is related to the incidence of HIV infection in the population. METHODS: We used integrated human-computer techniques to characterize the HIV-related tweets by male adolescents and young male adults (age range: 13-24 years). We identified tweets related to HIV risk and prevention by using natural language processing (NLP). Our NLP algorithm identified 89.1% (2243/2517) relevant tweets, which were manually coded by expert coders. We coded 1577 HIV-prevention tweets and 17.5% (940/5372) of general sex-related tweets (including emojis, gifs, and images), and we achieved reliability with intraclass correlation at 0.80 or higher on key constructs. Bivariate and multivariate analyses were performed to identify the spatial patterns in posting HIV-related tweets as well as the relationships between the tweets and local HIV infection rates. RESULTS: We analyzed 2517 tweets that were identified as relevant to HIV risk and prevention tags; these tweets were geolocated in 109 counties throughout the United States. After adjusting for region, HIV prevalence, and social disadvantage index, our findings indicated that every 100-tweet increase in HIV-specific tweets per capita from noninstitutional accounts was associated with a multiplicative effect of 0.97 (95% CI [0.94-1.00]; P=.04) on the incidence of HIV infections in the following year in a given county. CONCLUSIONS: Twitter may serve as a proxy of public behavior related to HIV infections, and the association between the number of HIV-related tweets and HIV infection rates further supports the use of social media for HIV disease prevention. JMIR Publications 2020-06-24 /pmc/articles/PMC7380998/ /pubmed/32579119 http://dx.doi.org/10.2196/17196 Text en ©Robin Stevens, Stephen Bonett, Jacqueline Bannon, Deepti Chittamuru, Barry Slaff, Safa K Browne, Sarah Huang, José A Bauermeister. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.06.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Stevens, Robin
Bonett, Stephen
Bannon, Jacqueline
Chittamuru, Deepti
Slaff, Barry
Browne, Safa K
Huang, Sarah
Bauermeister, José A
Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study
title Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study
title_full Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study
title_fullStr Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study
title_full_unstemmed Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study
title_short Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study
title_sort association between hiv-related tweets and hiv incidence in the united states: infodemiology study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7380998/
https://www.ncbi.nlm.nih.gov/pubmed/32579119
http://dx.doi.org/10.2196/17196
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