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Assessing Spurious Correlations in Big Search Data

Author

Listed:
  • Jesse T. Richman

    (Department of Political Science and Geography, Old Dominion University, BAL 7000, Norfolk, VA 23529, USA)

  • Ryan J. Roberts

    (Department of Public Service, Gardner-Webb University, Boiling Springs, NC 28017, USA)

Abstract

Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phenomena, measures that might serve an important role as leading indicators in forecasts and nowcasts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious ‘relationships’ between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies among probability distributions examined for random variables based upon gamma (1, 1) and Gaussian random walk distributions. Quantifying these spurious correlations and their likely magnitude for various distributions has value for several reasons. First, analysts can make progress toward accurate inference. Second, they can avoid unwarranted credulity. Third, they can demand appropriate disclosure from the study authors.

Suggested Citation

  • Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    • Handle: RePEc:gam:jforec:v:5:y:2023:i:1:p:15-296:d:1082814
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    References listed on IDEAS

    as
    1. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    2. Adrian Letchford & Tobias Preis & Helen Susannah Moat, 2016. "Quantifying the Search Behaviour of Different Demographics Using Google Correlate," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-11, February.
    3. Ahmed Shoukry Rashad, 2022. "The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai," Forecasting, MDPI, vol. 4(3), pages 1-11, July.
    4. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    Full references (including those not matched with items on IDEAS)

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