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Data-Driven Content Analysis of Social Media

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  • H. Andrew Schwartz
  • Lyle H. Ungar

Abstract

Researchers have long measured people’s thoughts, feelings, and personalities using carefully designed survey questions, which are often given to a relatively small number of volunteers. The proliferation of social media, such as Twitter and Facebook, offers alternative measurement approaches: automatic content coding at unprecedented scales and the statistical power to do open-vocabulary exploratory analysis. We describe a range of automatic and partially automatic content analysis techniques and illustrate how their use on social media generates insights into subjective well-being, health, gender differences, and personality.

Suggested Citation

  • H. Andrew Schwartz & Lyle H. Ungar, 2015. "Data-Driven Content Analysis of Social Media," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 78-94, May.
    • Handle: RePEc:sae:anname:v:659:y:2015:i:1:p:78-94
    DOI: 10.1177/0002716215569197
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    References listed on IDEAS

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    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
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    5. Kramer, Gerald H., 1983. "The Ecological Fallacy Revisited: Aggregate- versus Individual-level Findings on Economics and Elections, and Sociotropic Voting," American Political Science Review, Cambridge University Press, vol. 77(1), pages 92-111, March.
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    7. 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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