ArticleJournal of medical Internet research2020
Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of infodemiology studies using artificial intelligence: social media posts on HIV preexposure prophylaxis.AIDS (London, England) · 2025Pooled it
- Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis.Journal of medical Internet research · 2025Article
- Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review.Journal of medical Internet research · 2024Article
- From Virus to Viral: Content Analysis of HIV-related Twitter Messages among Young Men in the U.S.BMC digital health · 2024Article
- Online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP): an infodemiology study.BMC health services research · 2023Article
- Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review.Current HIV/AIDS reports · 2023Article
- Using geospatial social media data for infectious disease studies: a systematic review.International journal of digital earth · 2023Article
- Trends and Characteristics of #HIVPrevention Tweets Posted Between 2014 and 2019: Retrospective Infodemiology Study.JMIR public health and surveillance · 2022Article
- User- and Message-Level Correlates of Endorsement and Engagement for HIV-Related Messages on Twitter: Cross-sectional Study.JMIR public health and surveillance · 2022Article
- Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing.Yearbook of medical informatics · 2021Review
- Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review.JMIR public health and surveillance · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundAdolescents 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.
objectiveThe goal of this study was to examine how Twitter activity among young men is related to the incidence of HIV infection in the population.
methodsWe 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.
resultsWe 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.
conclusionsTwitter 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.
Indexed as
Identifiers
What OpenQuestion holds
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.