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Characterizing the Time-Perspective of Nations with Search Engine Query Data

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Listed:
  • Takao Noguchi
  • Neil Stewart
  • Christopher Y Olivola
  • Helen Susannah Moat
  • Tobias Preis

Abstract

Vast quantities of data on human behavior are being created by our everyday internet usage. Building upon a recent study by Preis, Moat, Stanley, and Bishop (2012), we used search engine query data to construct measures of the time-perspective of nations, and tested these measures against per-capita gross domestic product (GDP). The results indicate that nations with higher per-capita GDP are more focused on the future and less on the past, and that when these nations do focus on the past, it is more likely to be the distant past. These results demonstrate the viability of using nation-level data to build psychological constructs.

Suggested Citation

  • Takao Noguchi & Neil Stewart & Christopher Y Olivola & Helen Susannah Moat & Tobias Preis, 2014. "Characterizing the Time-Perspective of Nations with Search Engine Query Data," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-5, April.
    • Handle: RePEc:plo:pone00:0095209
    DOI: 10.1371/journal.pone.0095209
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    References listed on IDEAS

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    Cited by:

    1. Sudeep Bhatia, 2019. "Predicting Risk Perception: New Insights from Data Science," Management Science, INFORMS, vol. 65(8), pages 3800-3823, August.
    2. Macchia, Lucía & Plagnol, Anke C. & Reimers, Stian, 2018. "Does experience with high inflation affect intertemporal decision making? Sensitivity to inflation rates in Argentine and british delay discounting choices," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 75(C), pages 76-83.
    3. Merve Alanyali & Tobias Preis & Helen Susannah Moat, 2016. "Tracking Protests Using Geotagged Flickr Photographs," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-8, March.
    4. Daniel Read & Christopher Y. Olivola & David J. Hardisty, 2017. "The Value of Nothing: Asymmetric Attention to Opportunity Costs Drives Intertemporal Decision Making," Management Science, INFORMS, vol. 63(12), pages 4277-4297, December.
    5. T. T. Chen & B. Zheng & Y. Li & X. F. Jiang, 2017. "New approaches in agent-based modeling of complex financial systems," Papers 1703.06840, arXiv.org.
    6. Chen, Ting-Ting & Zheng, Bo & Li, Yan & Jiang, Xiong-Fei, 2018. "Information driving force and its application in agent-based modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 593-601.

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