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Political preferences nowcasting with factor analysis and internet data: The 2012 and 2016 US presidential elections

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  • Franch, Fabio

Abstract

This work shows how Internet data can be aggregated to track political candidates’ performance during a presidential campaign. The ‘wisdom of crowds’ theory is here exploited to a fuller extent using a unique combination of data sources and methodology. This is first done by taking medium-level aggregations from applications such as BetFair, InTrade, PredictIt, Twitter, Facebook, MySpace, YouTube, Instagram, Google, FiveThirtyEight, and then by synthesizing via factor analysis a qualitative measure of popularity for both candidates, while controlling for the intensity of electoral discussions. For two different elections, the methodology extracts popularity functions that closely reflect popularity swings occurring during/shortly after the presidential debates and other campaign-related events. The model presents itself as a cheaper and more accurate alternative to electoral polling being based on aggregate, anonymous data and voter's actions; for the same reason, it has the potential to address the “Shy Trump Supporters” bias. The model outperforms political betting markets and established platforms such as RealClearPolitics, FiveThirtyEight and Twitter, in addition to the factor model's original variables.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:tefoso:v:166:y:2021:i:c:s0040162521000998
    DOI: 10.1016/j.techfore.2021.120667
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