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YES or NO: Predicting the 2015 GReferendum results using Google Trends

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  • Mavragani, Amaryllis
  • Tsagarakis, Konstantinos P.

Abstract

We examine the possibility of predicting the 2015 Greek Referendum results by analyzing data from Google Trends on the ‘YES’ and ‘NO’ search terms. Our analysis shows that, despite the voting intention polls of the YES and NO votes being marginally one above the other throughout the prevoting period, the NO hits are clearly and every day above the YES ones, with statistically significant evidence. By analyzing data from Google Trends, we calculate a valid approximation of the final result, thus contributing to the discussion of using Google Trends as an elections' results prediction tool in the future.

Suggested Citation

  • Mavragani, Amaryllis & Tsagarakis, Konstantinos P., 2016. "YES or NO: Predicting the 2015 GReferendum results using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 1-5.
    • Handle: RePEc:eee:tefoso:v:109:y:2016:i:c:p:1-5
    DOI: 10.1016/j.techfore.2016.04.028
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    Cited by:

    1. Fronzetti Colladon, Andrea, 2020. "Forecasting election results by studying brand importance in online news," International Journal of Forecasting, Elsevier, vol. 36(2), pages 414-427.
    2. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    3. Laurell, Christofer & Sandström, Christian, 2018. "Comparing coverage of disruptive change in social and traditional media: Evidence from the sharing economy," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 339-344.
    4. Schadner, Wolfgang, 2022. "U.S. Politics from a multifractal perspective," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    5. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    6. Caetano, Marco Antonio Leonel, 2021. "Political activity in social media induces forest fires in the Brazilian Amazon," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    7. Jakob Bæk Kristensen & Thomas Albrechtsen & Emil Dahl-Nielsen & Michael Jensen & Magnus Skovrind & Tobias Bornakke, 2017. "Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-12, September.
    8. Fantazzini, Dean & Shakleina, Marina & Yuras, Natalia, 2018. "Big Data for computing social well-being indices of the Russian population," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 43-66.
    9. Aslanidis, Nektarios & Bariviera, Aurelio F. & López, Óscar G., 2022. "The link between cryptocurrencies and Google Trends attention," Finance Research Letters, Elsevier, vol. 47(PA).
    10. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    11. Jun, Seung-Pyo & Yoo, Hyoung Sun & Choi, San, 2018. "Ten years of research change using Google Trends: From the perspective of big data utilizations and applications," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 69-87.

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