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 - en rich ed 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 pers ist throughout the campaign and are only offset in the last days when a globalist upsurge brings the British Twittersphere closer to a divide between nationalis t and globalis t standpoints . Upon combining demographic variables with the classifier scores , we f i nd that the model explains 41% of the variance in the referendum vote, an indi cation 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.

Publication Type:	Article
Additional Information:	This is an Accepted Manuscript of an article published by Taylor & Francis Group in 'Information, Communicaation and Society' on 17/04/2014, available online: https://www.tandfonline.com/doi/full/10.1080/1369118X.2018.1433224.
Departments:	School of Policy & Global Affairs > Sociology & Criminology

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