An online risk index for the cross-sectional prediction of new HIV chlamydia, and gonorrhea diagnoses across U.S. counties and across years

Man Pui Sally Chan, Sophie Lohmann, Alex Morales, Chengxiang Zhai, Lyle Ungar, David R. Holtgrave, Dolores Albarracín

Research output: Contribution to journalArticle

Abstract

The present study evaluated the potential use of Twitter data for providing risk indices of STIs. We developed online risk indices (ORIs) based on tweets to predict new HIV, gonorrhea, and chlamydia diagnoses, across U.S. counties and across 5 years. We analyzed over one hundred million tweets from 2009 to 2013 using open-vocabulary techniques and estimated the ORIs for a particular year by entering tweets from the same year into multiple semantic models (one for each year). The ORIs were moderately to strongly associated with the actual rates (.35 < rs < .68 for 93% of models), both nationwide and when applied to single states (California, Florida, and New York). Later models were slightly better than older ones at predicting gonorrhea and chlamydia, but not at predicting HIV. The proposed technique using free social media data provides signals of community health at a high temporal and spatial resolution.

Original languageEnglish (US)
Pages (from-to)2322-2333
Number of pages12
JournalAIDS and Behavior
Volume22
Issue number7
DOIs
StatePublished - Jul 2018

Keywords

  • Big data
  • Chlamydia
  • Gonorrhea
  • HIV
  • Social media

ASJC Scopus subject areas

  • Social Psychology
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases

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