Twitter Analysis: Studying US Weekly Trends in Work Stress and Emotion

Wei Wang, Ivan Hernandez, Daniel A. Newman, Jibo He, Jiang Bian

Research output: Contribution to journalArticlepeer-review

Abstract

We propose the use of Twitter analysis as an alternative source of data to document weekly trends in emotion and stress, and attempt to use the method to estimate the work recovery effect of weekends. On the basis of 2,102,176,189 Tweets, we apply Pennebaker's linguistic inquiry word count (LIWC) approach to measure daily Tweet content across 18 months, aggregated to the US national level of analysis. We derived a word count dictionary to assess work stress and applied p-technique factor analysis to the daily word count data from 19 substantively different content areas covered by the LIWC dictionaries. Dynamic factor analysis revealed two latent factors in day-level variation of Tweet content. These two factors are: (a) a negative emotion/stress/somatic factor, and (b) a positive emotion/food/friends/home/family/leisure factor, onto which elements of work, money, achievement, and health issues have strong negative loadings. The weekly trend analysis revealed a clear "Friday dip" for work stress and negative emotion expressed on Twitter. In contrast, positive emotion Tweets showed a "mid-week dip" for Tuesday-Wednesday-Thursday and "weekend peak" for Friday through Sunday, whereas work/money/achievement/health problem Tweets showed a small "weekend dip" on Fridays through Sundays. Results partially support the Effort-Recovery theory. Implications and limitations of the method are discussed.

Original languageEnglish (US)
Pages (from-to)355-378
Number of pages24
JournalApplied Psychology
Volume65
Issue number2
DOIs
StatePublished - Apr 1 2016

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Applied Psychology

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