Action Tweets Linked to Reduced County-Level HIV Prevalence in the United States: Online Messages and Structural Determinants

Abstract HIV is uncommon in most US counties but travels quickly through vulnerable communities when it strikes. Tracking behavior through social media may provide an unobtrusive, naturalistic means of predicting HIV outbreaks and understanding the behavioral and psychological factors that increase communities’ risk. General action goals, or the motivation to engage in cognitive and motor activity, may support protective health behavior (e.g., using condoms) or encourage activity indiscriminately (e.g., risky sex), resulting in mixed health effects. We explored these opposing hypotheses by regressing county-level HIV prevalence on action language (e.g., work, plan) in over 150 million tweets mapped to US counties. Controlling for demographic and structural predictors of HIV, more active language was associated with lower HIV rates. By leveraging language used on social media to improve existing predictive models of geographic variation in HIV, future targeted HIV-prevention interventions may have a better chance of reaching high-risk communities before outbreaks occur.

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