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

Franch, Fabio

doi:10.1016/j.techfore.2021.120667

Cited by 3 publications

(1 citation statement)

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“…Despite their popularity (Boice & Wezerek, 2019), traditional polling and public opinion methods are costly, often requiring large and representative samples of the electorate to understand perceptions of candidates. Recent advances in quantitative and computational modeling, however, have used different sources of digital information (e.g., social media data) to predict race outcomes (Franch, 2021), collecting posts and online conversations about politicians to understand how the public feels about candidates and their messaging. Social media data are often free, relatively easy to collect, and naturallyproduced, offering an ideal setting for political pollsters to evaluate public opinion and test their assumptions about an electorate in real time.…”

Section: Language Patterns On Social Media Predict Winners Of Us Cong...mentioning

confidence: 99%

Language Patterns on Social Media Predict Winners of US Congressional Races

Markowitz¹

2022

Preprint

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Political races are often predicted by polls, but such investigations are costly to conduct and the results can be elusive because of their self-report nature. Alternative quantitative modeling approaches are promising to alleviate the prior concerns and therefore, this paper predicted outcomes of the 2020 US Congress elections from computational analyses of language data. Psychological profiles of 918 candidates were created from their social media records (N = 2,836,114 Tweets) and three preregistered dimensions — common words, analytic thinking, and affect — were associated with race outcome. Congressional race winners wrote with common words, communicated in an analytic style, and used more positive affect than congressional race losers. The predictive accuracy of these language dimensions alone was substantial (AUC = 0.844; 79.8% 10-fold cross-validated accuracy, 95% CI [73.2%, 85.4%]), values consistent with or exceeding most polling accuracies. Together, social media language data reveal psychological information about politicians and can indicate race outcomes.

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Section: Language Patterns On Social Media Predict Winners Of Us Cong...mentioning

confidence: 99%