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Can we vote with our tweet? On the perennial difficulty of election forecasting with social media

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  • Huberty, Mark

Abstract

Social media and other “big” data promise new sources of information for tracking and forecasting electoral contests in democratic societies. This paper discusses the use of social media, and Twitter in particular, for forecasting elections in the United States, Germany, and other democracies. All known forecasting methods based on social media have failed when subjected to the demands of true forward-looking electoral forecasting. These failures appear to be due to fundamental properties of social media, rather than to methodological or algorithmic difficulties. In short, social media do not, and probably never will, offer a stable, unbiased, representative picture of the electorate; and convenience samples of social media lack sufficient data to fix these problems post hoc. Hence, while these services may, as others in this volume discuss, offer new ways of reaching prospective voters, the data that they generate will not replace polling as a means of assessing the sentiment or intentions of the electorate.

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  • 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.
  • Handle: RePEc:eee:intfor:v:31:y:2015:i:3:p:992-1007
    DOI: 10.1016/j.ijforecast.2014.08.005
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    Cited by:

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    2. Stefan Stieglitz & Christian Meske & Björn Ross & Milad Mirbabaie, 2020. "Going Back in Time to Predict the Future - The Complex Role of the Data Collection Period in Social Media Analytics," Information Systems Frontiers, Springer, vol. 22(2), pages 395-409, April.
    3. 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.
    4. Ronald McDonald & Xuxin Mao, 2015. "Forecasting the 2015 General Election with Internet Big Data: An Application of the TRUST Framework," Working Papers 2016_03, Business School - Economics, University of Glasgow.
    5. Green, Lawrence & Sung, Ming-Chien & Ma, Tiejun & Johnson, Johnnie E. V., 2019. "To what extent can new web-based technology improve forecasts? Assessing the economic value of information derived from Virtual Globes and its rate of diffusion in a financial market," European Journal of Operational Research, Elsevier, vol. 278(1), pages 226-239.
    6. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    7. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    8. Amador Diaz Lopez Julio Cesar & Collignon-Delmar Sofia & Benoit Kenneth & Matsuo Akitaka, 2017. "Predicting the Brexit Vote by Tracking and Classifying Public Opinion Using Twitter Data," Statistics, Politics and Policy, De Gruyter, vol. 8(1), pages 85-104, October.
    9. Ali, Maged & Gomes, Lucas Moreira & Azab, Nahed & de Moraes Souza, João Gabriel & Sorour, M. Karim & Kimura, Herbert, 2023. "Panic buying and fake news in urban vs. rural England: A case study of twitter during COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    10. Brown, Alasdair & Reade, J. James & Vaughan Williams, Leighton, 2019. "When are prediction market prices most informative?," International Journal of Forecasting, Elsevier, vol. 35(1), pages 420-428.
    11. Franch, Fabio, 2021. "Political preferences nowcasting with factor analysis and internet data: The 2012 and 2016 US presidential elections," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    12. 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.
    13. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    14. Carlos Arcila-Calderón & David Blanco-Herrero & Maximiliano Frías-Vázquez & Francisco Seoane-Pérez, 2021. "Refugees Welcome? Online Hate Speech and Sentiments in Twitter in Spain during the Reception of the Boat Aquarius," Sustainability, MDPI, vol. 13(5), pages 1-16, March.
    15. Li, Xixi & Bai, Yun & Kang, Yanfei, 2022. "Exploring the social influence of the Kaggle virtual community on the M5 competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1507-1518.
    16. Gutierrez-Barroso Josue & Báez-García Alberto Javier & Flores-Muñoz Francisco & Ruiz Medina Luis Javier & Trujillo González Juan Vianney & Padrón-Armas Ana Goretty, 2024. "Google Trends of political parties in Europe: a fractal exploration," Central European Journal of Public Policy, Sciendo, vol. 18(1), pages 24-36.

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