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Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study
被引:9
|作者:
Stevens, Robin
[1
]
Bonett, Stephen
[1
]
Bannon, Jacqueline
[1
]
Chittamuru, Deepti
[2
]
Slaff, Barry
[3
]
Browne, Safa K.
[4
]
Huang, Sarah
[1
]
Bauermeister, Jose A.
[1
]
机构:
[1] Univ Penn, Sch Nursing, Dept Family & Community Hlth, 416 Curie Blvd, Philadelphia, PA 19104 USA
[2] Univ Calif Merced, Merced, CA USA
[3] Univ Penn, Philadelphia, PA 19104 USA
[4] Childrens Hosp Penn, Philadelphia, PA USA
关键词:
HIV/AIDS;
social media;
youth;
natural language processing;
surveillance;
PREVALENCE;
TWITTER;
D O I:
10.2196/17196
中图分类号:
R19 [保健组织与事业(卫生事业管理)];
学科分类号:
摘要:
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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