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Asking about social circles improves election predictions

Author

Listed:
  • M. Galesic

    (Santa Fe Institute
    Max Planck Institute for Human Development)

  • W. Bruine de Bruin

    (Leeds University Business School, University of Leeds
    Carnegie Mellon University
    University of Southern California)

  • M. Dumas

    (Santa Fe Institute
    London School of Economics)

  • A. Kapteyn

    (University of Southern California)

  • J. E. Darling

    (University of Southern California
    VHA Greater Los Angeles Health Care System)

  • E. Meijer

    (University of Southern California)

Abstract

Election outcomes can be difficult to predict. A recent example is the 2016 US presidential election, in which Hillary Clinton lost five states that had been predicted to go for her, and with them the White House. Most election polls ask people about their own voting intentions: whether they will vote and, if so, for which candidate. We show that, compared with own-intention questions, social-circle questions that ask participants about the voting intentions of their social contacts improved predictions of voting in the 2016 US and 2017 French presidential elections. Responses to social-circle questions predicted election outcomes on national, state and individual levels, helped to explain last-minute changes in people’s voting intentions and provided information about the dynamics of echo chambers among supporters of different candidates.

Suggested Citation

  • M. Galesic & W. Bruine de Bruin & M. Dumas & A. Kapteyn & J. E. Darling & E. Meijer, 2018. "Asking about social circles improves election predictions," Nature Human Behaviour, Nature, vol. 2(3), pages 187-193, March.
    • Handle: RePEc:nat:nathum:v:2:y:2018:i:3:d:10.1038_s41562-018-0302-y
    DOI: 10.1038/s41562-018-0302-y
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    Cited by:

    1. Olsson, Henrik, 2021. "Election polling is not dead: A Bayesian bootstrap method yields accurate forecasts," OSF Preprints nqcgs, Center for Open Science.
    2. Schönegger, Philipp & Verheyen, Steven, 2022. "Taking A Closer Look At The Bayesian Truth Serum: A Registered Report (Stage 2 Registered Report)," OSF Preprints 9zvqj, Center for Open Science.
    3. Hamish Greenop‐Roberts, 2022. "Forecasting Federal Elections: New Data From 2010–2019 and a Discussion of Alternative and Emerging Methods," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 55(1), pages 25-39, March.
    4. Rajiv Sethi & Julie Seager & Emily Cai & Daniel M. Benjamin & Fred Morstatter, 2021. "Models, Markets, and the Forecasting of Elections," Papers 2102.04936, arXiv.org, revised May 2021.
    5. Malik, Muhammad Yousaf & Latif, Kashmala, 2021. "Impact of outbound tourism on outward FDI," Annals of Tourism Research, Elsevier, vol. 91(C).
    6. Sonja Radas & Drazen Prelec, 2019. "Whose data can we trust: How meta-predictions can be used to uncover credible respondents in survey data," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-16, December.

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