Voting intentions on social media and political opinion polls

Pekar, Viktor; Najafi, Hossein; Binner, Jane; Swanson, Riley; Rickard, Charles; Fry, John

Abstract

Opinion polls play an important role in modern democratic processes: they are known to not only affect the outcomes of elections, but also have a significant influence on government policy after elections. Recent years have seen large discrepancies between polls and outcomes at several major elections and referendums, stemming from decreased participation in polls and an increasingly volatile electorate. This calls for new ways to measure public support for political parties. In this paper, we propose a method for measuring the popularity of election candidates on social media using Machine Learning-based Natural Language Processing techniques. The method is based on detecting voting intentions in the data. This is a considerable advance upon earlier work using automatic sentiment analysis. We evaluate the method both intrinsically on a set of hand-labelled social media posts, and extrinsically – by forecasting daily election polls. In the extrinsic evaluation, we analyze data from the 2016 US presidential election, and find that voting intentions measured from social media provide significant additional predictive value for forecasting daily polls. Thus, we demonstrate that the proposed method can be used to interpolate polls both spatially and temporally, thus providing reliable, continuous and fine-grained information about public opinion on current political issues.

Citation

Pekar, V., Najafi, H., Binner, J., Swanson, R., Rickard, C., & Fry, J. (in press). Voting intentions on social media and political opinion polls. Government information quarterly, Article 101658. https://doi.org/10.1016/j.giq.2021.101658

• Journal Article Type: Article
• Acceptance Date: Nov 18, 2021
• Online Publication Date: Nov 25, 2021
• Deposit Date: Feb 9, 2022
• Publicly Available Date: May 26, 2023
• Journal: Government information quarterly
• Print ISSN: 0740-624X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Article Number: 101658
• DOI: https://doi.org/10.1016/j.giq.2021.101658
• Public URL: https://hull-repository.worktribe.com/output/3921116