An Online Risk Index for the Cross-Sectional Prediction of New HIV Chlamydia, and Gonorrhea Diagnoses Across U.S. Counties and Across Years.
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...
| Published in: | AIDS & Behavior Vol. 22; no. 7; pp. 2322 - 2334 |
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| Main Authors: | , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Springer Nature
Jul2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130360666&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130360666 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10907165 G1T jtl: AIDS & Behavior issn: 10907165 maglogo: N pubinfo: dt: Jul2018 vid: 22 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 130360666 130360666 130360666 10.1007/s10461-018-2046-0 130360666 ppf: 2322 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Online Risk Index for the Cross-Sectional Prediction of New HIV Chlamydia, and Gonorrhea Diagnoses Across U.S. Counties and Across Years. aug: au: Chan, Man-pui Sally Lohmann, Sophie Albarracín, Dolores Morales, Alex Zhai, Chengxiang Ungar, Lyle Holtgrave, David R. affil: Department of Psychology, University of Illinois at Urbana-Champaign, 61820, Champaign, IL, USA sug: subj: HIV Infections Risk Factors Gonorrhea Diagnosis Chlamydia Infections Human Cross Sectional Studies Online Services Outcomes (Health Care) Social Media United States Geographic Locations Vocabulary Semantics Spatial Behavior Sexually Transmitted Diseases Data Analysis Software ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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