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Can Twitter be used to predict county excessive alcohol consumption rates?

Author

Listed:
  • Brenda Curtis
  • Salvatore Giorgi
  • Anneke E K Buffone
  • Lyle H Ungar
  • Robert D Ashford
  • Jessie Hemmons
  • Dan Summers
  • Casey Hamilton
  • H Andrew Schwartz

Abstract

Objectives: The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county. Methods: Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis. Results: Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. ‘ready gettin leave’) can explain much of the variance associated between socioeconomics and excessive alcohol consumption. Conclusions: Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.

Suggested Citation

  • Brenda Curtis & Salvatore Giorgi & Anneke E K Buffone & Lyle H Ungar & Robert D Ashford & Jessie Hemmons & Dan Summers & Casey Hamilton & H Andrew Schwartz, 2018. "Can Twitter be used to predict county excessive alcohol consumption rates?," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
    • Handle: RePEc:plo:pone00:0194290
    DOI: 10.1371/journal.pone.0194290
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    References listed on IDEAS

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    2. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    3. H Andrew Schwartz & Johannes C Eichstaedt & Margaret L Kern & Lukasz Dziurzynski & Stephanie M Ramones & Megha Agrawal & Achal Shah & Michal Kosinski & David Stillwell & Martin E P Seligman & Lyle H U, 2013. "Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
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    Cited by:

    1. Chee Siang Ang & Ranjith Venkatachala, 2023. "Generalizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns," Societies, MDPI, vol. 13(5), pages 1-19, May.
    2. Martina Jakob & Sebastian Heinrich, 2023. "Measuring Human Capital with Social Media Data and Machine Learning," University of Bern Social Sciences Working Papers 46, University of Bern, Department of Social Sciences.
    3. Salvatore Giorgi & David B. Yaden & Johannes C. Eichstaedt & Robert D. Ashford & Anneke E.K. Buffone & H. Andrew Schwartz & Lyle H. Ungar & Brenda Curtis, 2020. "Cultural Differences in Tweeting about Drinking Across the US," IJERPH, MDPI, vol. 17(4), pages 1-14, February.

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