title: Future-oriented tweets predict lower county-level HIV prevalence in the United States.
abstract: OBJECTIVE
  Future orientation promotes health and well-being at the individual level. Computerized text analysis of a dataset encompassing billions of words used across the United States on Twitter tested whether community-level rates of future-oriented messages correlated with lower human immunodeficiency virus (HIV) rates and moderated the association between behavioral risk indicators and HIV.
  
  
  METHODS
  Over 150 million tweets mapped to U.S. counties were analyzed using 2 methods of text analysis. First, county-level HIV rates (cases per 100,000) were regressed on aggregate usage of future-oriented language (e.g., will, gonna). A second data-driven method regressed HIV rates on individual words and phrases.
  
  
  RESULTS
  Results showed that counties with higher rates of future tense on Twitter had fewer HIV cases, independent of strong structural predictors of HIV such as population density. Future-oriented messages also appeared to buffer health risk: Sexually transmitted infection rates and references to risky behavior on Twitter were associated with higher HIV prevalence in all counties except those with high rates of future orientation. Data-driven analyses likewise showed that words and phrases referencing the future (e.g., tomorrow, would be) correlated with lower HIV prevalence.
  
  
  CONCLUSIONS
  Integrating big data approaches to text analysis and epidemiology with psychological theory may provide an inexpensive, real-time method of anticipating outbreaks of HIV and etiologically similar diseases.
authors.affiliation: Department of Psychological Sciences, Texas Tech University.
authors.forename: Molly E
authors.initials: ME
authors.lastname: Ireland
authors.affiliation: Department of Psychology, University of Pennsylvania.
authors.forename: H Andrew
authors.initials: HA
authors.lastname: Schwartz
authors.affiliation: Annenberg School for Communication, University of Pennsylvania.
authors.forename: Qijia
authors.initials: Q
authors.lastname: Chen
authors.affiliation: Department of Psychology, University of Pennsylvania.
authors.forename: Lyle H
authors.initials: LH
authors.lastname: Ungar
authors.affiliation: Psychology Department, University of Illinois at Urbana-Champaign.
authors.forename: Dolores
authors.initials: D
authors.lastname: Albarracín
mesh_terms: D005260:Female; D005544:Forecasting; D015658:HIV Infections / Q000175:diagnosis / Q000453:epidemiology* / Q000523:psychology*; D006801:Humans; D008297:Male; D015995:Prevalence; D012309:Risk-Taking; D061108:Social Media / Q000639:trends*; D014481:United States / Q000453:epidemiology