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

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Marco Bastos is Lecturer in Media and Communication in the Department of Sociology at City, University of London and an affiliate of Duke University’s Network Analysis Center.

Dan Mercea is Senior Lecturer in the Department of Sociology at City, University of London. He is the author of Civic Participation in Contentious Politics: The Digital Foreshadowing of Protest.

ORCID

Marco Bastos http://orcid.org/0000-0003-0480-1078

Additional information

Funding

This work was supported by the Research Pump-Priming Fund of the School of Arts and Social Sciences of City, University of London.