Asking about social circles improves election predictions

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1
Fig. 2: The extent of 'echo chambers' among Clinton and Trump voters, over time.

References

  1. 1.

    Rothschild, D. M. & Wolfers, J. Forecasting elections: voter intentions versus expectations. SSRN https://doi.org/10.2139/ssrn.1884644 (2011).

  2. 2.

    Lewis-Beck, M. S. & Skalaban, A. Citizen forecasting: can voters see into the future? Br. J. Polit. Sci. 19, 146–153 (1989).

    Article  Google Scholar 

  3. 3.

    Graefe, A. Accuracy of vote expectation surveys in forecasting elections. Public Opin. Q. 78, 204–232 (2014).

    Article  Google Scholar 

  4. 4.

    Irwin, G. A. & Van Holsteyn, J. J. M. According to the polls: the influence of opinion polls on expectations. Public Opin. Q. 66, 92–104 (2002).

    Article  Google Scholar 

  5. 5.

    Nisbett, R. E. & Kunda, Z. Perception of social distributions. J. Pers. Soc. Psychol. 48, 297–311 (1985).

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Dawtry, R. J., Sutton, R. M. & Sibley, C. G. Why wealthier people think people are wealthier, and why it matters: from social sampling to attitudes to redistribution. Psychol. Sci. 26, 1389–1400 (2015).

    Article  PubMed  Google Scholar 

  7. 7.

    Galesic, M., Olsson, H. & Rieskamp, J. A sampling model of social judgment. Psychol. Rev. (in the press).

  8. 8.

    Galesic, M., Olsson, H. & Rieskamp, J. Social sampling explains apparent biases in judgments of social environments. Psychol. Sci. 23, 1515–1523 (2012).

    Article  PubMed  Google Scholar 

  9. 9.

    Sudman, S. & Bradburn, N. M. Asking Questions: A Practical Guide to Questionnaire Design (Jossey-Bass, San Francisco, CA, 1982).

  10. 10.

    Darling, J. & Kapteyn, A. Hidden Trump Voters? (AAPOR, 2017).

  11. 11.

    Huckfeldt, R. R. & Sprague, J. Citizens, Politics, and Social Communication: Information and Influence in an Election Campaign (Cambridge Univ. Press, Cambridge, 1995).

  12. 12.

    Sinclair, B. The Social Citizen: Peer Networks and Political Behavior (Univ. Chicago Press, Chicago, IL, 2012).

  13. 13.

    Groves, R. Survey Errors and Survey Costs (Wiley, Hoboken, NJ, 2004).

  14. 14.

    2016 election survey. GfK http://www.gfk.com/products-a-z/us/public-communications-and-social-science/2016-election-survey (2016).

  15. 15.

    The USC Dornsife/LA Times presidential election “daybreak” poll—understanding America study. CESRUSC http://cesrusc.org/election/ (2016).

  16. 16.

    Kapteyn, A., Meijer, E. & Weerman, B. Methodology of the RAND Continuous 2012 Presidential Election Poll Working Paper No. WR-961 (RAND Corporation, 2012).

  17. 17.

    Delavande, A. & Manski, C. F. Probabilistic polling and voting in the 2008 presidential election: evidence from the American Life Panel. Public Opin. Q. 74, 433–459 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Gutsche, T. L., Kapteyn, A., Meijer, E. & Weerman, B. The RAND Continuous 2012 Presidential Election Poll. Public Opin. Q. 78, 233–254 (2014).

    Article  Google Scholar 

  19. 19.

    Mosteller, F. & Doob, L. W. The Pre-Election Polls of 1948 (Social Science Research Council, 1949).

  20. 20.

    Martin, E. A., Traugott, M. W. & Kennedy, C. A review and proposal for a new measure of poll accuracy. Public Opin. Q. 69, 342–369 (2005).

    Article  Google Scholar 

  21. 21.

    Arzheimer, K. & Evans, J. A new multinomial accuracy measure for polling bias. Polit. Anal. 22, 31–44 (2014).

    Article  Google Scholar 

  22. 22.

    Silver, N. Election update: the state of the States. FiveThirtyEight https://fivethirtyeight.com/features/election-update-the-state-of-the-states/ (2016).

  23. 23.

    National polls. FiveThirtyEight https://projects.fivethirtyeight.com/2016-election-forecast/national-polls/ (2016).

  24. 24.

    Abrigo, M. R. & Love, I. Estimation of panel vector autoregression in stata: a package of programs. Stata J. 16, 778–804 (2016).

    Google Scholar 

  25. 25.

    Holtz-Eakin, D., Newey, W. & Rosen, H. S. Estimating vector autoregressions with panel data. Econometrica 56, 1371–1395 (1988).

    Article  Google Scholar 

  26. 26.

    Dawes, R. Statistical criteria for establishing a truly false consensus effect. J. Exp. Soc. Psychol. 25, 1–17 (1989).

    Article  Google Scholar 

  27. 27.

    Swift, A. Americans’ trust in mass media sinks to new low. Gallup http://www.gallup.com/poll/195542/americans-trust-mass-media-sinks-new-low.aspx (2016).

  28. 28.

    Duggan, M. & Smith, A. The tone of social media discussions around politics. Pew Research Center http://www.pewinternet.org/2016/10/25/the-tone-of-social-media-discussions-around-politics/ (2016).

  29. 29.

    Noelle-Neumann, E. The spiral of silence. J. Commun. 24, 43–51 (1974).

    Article  Google Scholar 

  30. 30.

    Thompson, A. Parallel narratives. Vice News https://news.vice.com/story/journalists-and-trump-voters-live-in-separate-online-bubbles-mit-analysis-shows (2016).

  31. 31.

    Tetlock, P. E. & Gardner, D. Superforecasting: The Art and Science of Prediction (Random House, New York, NY, 2016).

  32. 32.

    Prelec, D. A Bayesian truth serum for subjective data. Science 306, 462–466 (2004).

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Prelec, D., Seung, H. S. & McCoy, J. A solution to the single-question crowd wisdom problem. Nature 541, 532–535 (2017).

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Cahaly, R. Trafalgar Group: most accurate polls in battleground states (FL, PA, MI, NC, OH, CO, GA) and most accurate electoral projection 306 Trump – 232 Clinton. Trafalgar Group (11 September 2016); http://us13.campaign-archive1.com/?e=9941a03e2e&u=99839c1f5b2cbb6320408fcb8&id=f7b7326d0b

  35. 35.

    Alabama—2016 election forecast. FiveThirtyEight https://projects.fivethirtyeight.com/2016-election-forecast/alabama/ (2016).

  36. 36.

    Opinion polling for the French presidential election, 2017. Wikipedia https://en.wikipedia.org/wiki/Opinion_polling_for_the_French_presidential_election,_2017 (2017).

  37. 37.

    Sample and recruitment—understanding America Study. USC Dornsife https://uasdata.usc.edu/index.php (2016).

  38. 38.

    Meijer, E. Sample Selection in the Daybreak Poll (CESRUSC, 2016); http://cesrusc.org/election/samp-estim01.pdf

  39. 39.

    Meijer, E. Weighting the Daybreak Poll (CESRUSC, 2016); http://cesrusc.org/election/weights03.pdf

  40. 40.

    KnowledgePanel. GfK http://www.gfk.com/products-a-z/us/knowledgepanel-united-states/ (2016).

  41. 41.

    Thomas, R. K., Barlas, F. M., Weber, A. & McPetrie, L. Election 2016 Survey—National Election Omnibus (GfK Custom Research, 2016).

  42. 42.

    Norpoth, H. A win for the pollsters: French election predicted accurately. The Hill http://thehill.com/blogs/pundits-blog/international-affairs/330272-after-rough-2016-pollsters-get-it-right-in-french (2017).

  43. 43.

    Cousineau, D. Confidence intervals in within-subject designs: a simpler solution to Loftus and Masson’s method. Tutor. Quant. Methods Psychol. 1, 42–45 (2005).

    Article  Google Scholar 

Download references

Acknowledgements

The project described in this paper relies in part on data from surveys administered by the UAS, which is maintained by the Center for Economic and Social Research (CESR) at the USC. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of USC or UAS. This project was supported in part by grants from the National Science Foundation (MMS-1560592), the Swedish Foundation for the Humanities and the Social Sciences (Riksbankens Jubileumsfond) Program on Science and Proven Experience. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies. We thank H. Olsson for helpful comments on a preliminary version of the paper and A. Todd for editing the manuscript.

Author information

Affiliations

Authors

Contributions

M.G. and W.B.d.B. designed the research questions and the social-circle measures. A.K., J.E.D. and E.M. designed the data collection methods and collected the data within the USC study. M.G. and E.M. analysed the US data. M.D. translated and adjusted the materials for data collection in France and analysed the French data. All authors contributed to the writing of the paper.

Corresponding author

Correspondence to M. Galesic.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1–3, Supplementary Tables 1–6.

Life Sciences Reporting Summary

Supplementary Data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Galesic, M., Bruine de Bruin, W., Dumas, M. et al. Asking about social circles improves election predictions. Nat Hum Behav 2, 187–193 (2018). https://doi.org/10.1038/s41562-018-0302-y

Download citation

Further reading