Parametrizing Brexit: Mapping Twitter Political Space to Parliamentary Constituencies

Abstract

In this paper, a proof of concept study is performed to validate the use of social media signal to model the ideological coordinates underpinning the Brexit debate. We rely on geographically-enriched Twitter data and a purpose-built, deep learning algorithm to map the political value space of users tweeting the referendum onto Parliamentary Constituencies. We find a significant incidence of nationalist sentiments and economic views expressed on Twitter, which persist throughout the campaign and are only offset in the last days when a globalist upsurge brings the British Twittersphere closer to a divide between nationalist and globalist standpoints. Upon combining demographic variables with the classifier scores, we find that the model explains 41% of the variance in the referendum vote, an indication that not only material inequality, but also ideological readjustments have contributed to the outcome of the referendum. We conclude with a discussion of conceptual and methodological challenges in signal-processing social media data as a source for the measurement of public opinion.

Keywords: Brexit, Twitter, Public Opinion, Machine Learning, Nationalism, Populism, Referendum