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A new insight into combining forecasts for elections: The role of social media

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  • Chih‐Yu Chin
  • Cheng‐Lung Wang

Abstract

This study is devoted to gain insight into a timely, accurate, and relevant combining forecast by considering social media (Facebook), opinion polls, and prediction markets. We transformed each type of raw data into the possibility of victory as a forecasting model. Besides the four single forecasts, namely Facebook fans, Facebook “people talking about this” (PTAT) statistics, opinion polls, and prediction markets, we generated three combined forecasts by associating various combinations of the four components. Then, we examined the predictive performance of each forecast on vote shares and the elected/non‐elected outcome across the election period. Our findings, based on the evidence of Taiwan's 2018 county and city elections, showed that incorporating the Facebook PTAT statistic with polls and prediction markets generates the most powerful forecast. Moreover, we recognized the matter of the time horizons where the best proposed model has better accuracy gains in prediction—in the “late of election,” but not in “approaching election”. The patterns of the trend of accuracy across time for each forecasting model also differ from one another. We also highlighted the complementarity of various types of data in the paper because each forecast makes important contributions to forecasting elections.

Suggested Citation

  • Chih‐Yu Chin & Cheng‐Lung Wang, 2021. "A new insight into combining forecasts for elections: The role of social media," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 132-143, January.
  • Handle: RePEc:wly:jforec:v:40:y:2021:i:1:p:132-143
    DOI: 10.1002/for.2711
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    References listed on IDEAS

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    1. Andrew Leigh & Justin Wolfers, 2006. "Competing Approaches to Forecasting Elections: Economic Models, Opinion Polling and Prediction Markets," The Economic Record, The Economic Society of Australia, vol. 82(258), pages 325-340, September.
    2. Leighton Vaughan Williams & J. James Reade, 2016. "Forecasting Elections," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(4), pages 308-328, July.
    3. Rothschild, David, 2015. "Combining forecasts for elections: Accurate, relevant, and timely," International Journal of Forecasting, Elsevier, vol. 31(3), pages 952-964.
    4. Xiaodong Zhang, 2018. "Social media popularity and election results: A study of the 2016 Taiwanese general election," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-17, November.
    5. Sveinung Arnesen & Ole Bergfjord, 2014. "Prediction markets vs polls – an examination of accuracy for the 2008 and 2012 elections," Journal of Prediction Markets, University of Buckingham Press, vol. 8(3), pages 24-33.
    6. repec:cup:judgdm:v:14:y:2019:i:2:p:135-147 is not listed on IDEAS
    7. Huberty, Mark, 2015. "Can we vote with our tweet? On the perennial difficulty of election forecasting with social media," International Journal of Forecasting, Elsevier, vol. 31(3), pages 992-1007.
    8. Lionel Page, 2008. "Comparing Prediction Market Prices and Opinion Polls in Political Elections," Journal of Prediction Markets, University of Buckingham Press, vol. 2(1), pages 91-97, May.
    9. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
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    Cited by:

    1. Francisco Vergara-Perucich, 2022. "Assessing the Accuracy of Google Trends for Predicting Presidential Elections: The Case of Chile, 2006–2021," Data, MDPI, vol. 7(11), pages 1-12, October.

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