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Situations in 140 Characters: Assessing Real-World Situations on Twitter

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  • David G Serfass
  • Ryne A Sherman

Abstract

Over 20 million Tweets were used to study the psychological characteristics of real-world situations over the course of two weeks. Models for automatically and accurately scoring individual Tweets on the DIAMONDS dimensions of situations were developed. Stable daily and weekly fluctuations in the situations that people experience were identified. Predicted temporal trends were found, providing validation for this new method of situation assessment. On weekdays, Duty peaks in the midmorning and declines steadily thereafter while Sociality peeks in the evening. Negativity is highest during the workweek and lowest on the weekends. pOsitivity shows the opposite pattern. Additionally, gender and locational differences in the situations shared on Twitter are explored. Females share both more emotionally charged (pOsitive and Negative) situations, while no differences were found in the amount of Duty experienced by males and females. Differences in the situations shared from Rural and Urban areas were not found. Future applications of assessing situations using social media are discussed.

Suggested Citation

  • David G Serfass & Ryne A Sherman, 2015. "Situations in 140 Characters: Assessing Real-World Situations on Twitter," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-19, November.
    • Handle: RePEc:plo:pone00:0143051
    DOI: 10.1371/journal.pone.0143051
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    References listed on IDEAS

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    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. 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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    1. C. A. Piña-García & J. Mario Siqueiros-García & E. Robles-Belmont & Gustavo Carreón & Carlos Gershenson & Julio Amador Díaz López, 2018. "From neuroscience to computer science: a topical approach on Twitter," Journal of Computational Social Science, Springer, vol. 1(1), pages 187-208, January.

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