Data-Driven Content Analysis of Social Media

H. Andrew Schwartz and Lyle H. Ungar

The ANNALS of the American Academy of Political and Social Science, 2015, vol. 659, issue 1, 78-94

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.

Keywords: content analysis; text mining; social media; Twitter; Facebook (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:sae:anname:v:659:y:2015:i:1:p:78-94

DOI: 10.1177/0002716215569197

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