Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes.

Sean D Young, Caitlin Rivers, Bryan Lewis
Author Information
  1. Sean D Young: Department of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. Electronic address: sdyoung@mednet.ucla.edu.
  2. Caitlin Rivers: Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA.
  3. Bryan Lewis: Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA.

Abstract

OBJECTIVE: Recent availability of "big data" might be used to study whether and how sexual risk behaviors are communicated on real-time social networking sites and how data might inform HIV prevention and detection. This study seeks to establish methods of using real-time social networking data for HIV prevention by assessing 1) whether geolocated conversations about HIV risk behaviors can be extracted from social networking data, 2) the prevalence and content of these conversations, and 3) the feasibility of using HIV risk-related real-time social media conversations as a method to detect HIV outcomes.
METHODS: In 2012, tweets (N=553,186,061) were collected online and filtered to include those with HIV risk-related keywords (e.g., sexual behaviors and drug use). Data were merged with AIDSVU data on HIV cases. Negative binomial regressions assessed the relationship between HIV risk tweeting and prevalence by county, controlling for socioeconomic status measures.
RESULTS: Over 9800 geolocated tweets were extracted and used to create a map displaying the geographical location of HIV-related tweets. There was a significant positive relationship (p<.01) between HIV-related tweets and HIV cases.
CONCLUSION: Results suggest the feasibility of using social networking data as a method for evaluating and detecting Human immunodeficiency virus (HIV) risk behaviors and outcomes.

Keywords

References

  1. Cyberpsychol Behav Soc Netw. 2013 Apr;16(4):243-7 [PMID: 23438268]
  2. AIDS Behav. 2012 Oct;16(7):1743-5 [PMID: 22821067]
  3. PLoS One. 2010 Nov 29;5(11):e14118 [PMID: 21124761]
  4. J Med Internet Res. 2011 May 13;13(2):e38 [PMID: 21571632]
  5. BMC Public Health. 2011 Jul 21;11:583 [PMID: 21777470]
  6. Sex Transm Dis. 2013 Feb;40(2):162-7 [PMID: 23324979]
  7. PLoS Comput Biol. 2012;8(7):e1002616 [PMID: 22844241]
  8. Ann Intern Med. 2013 Sep 3;159(5):318-24 [PMID: 24026317]
  9. Top HIV Med. 2006 Jun-Jul;14(2):84-7 [PMID: 16835463]
  10. PLoS One. 2013 May 01;8(5):e62271 [PMID: 23658716]
  11. Am J Prev Med. 2012 Nov;43(5):467-74 [PMID: 23079168]

Grants

  1. K01 MH090884/NIMH NIH HHS
  2. P30 MH058107/NIMH NIH HHS
  3. UL1 TR000124/NCATS NIH HHS

MeSH Term

Disease Outbreaks
HIV Infections
Humans
Internet
Prevalence
Public Health
Social Media
United States

Word Cloud

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