an article by Alexander Nurse and Olivier Sykes (University of Liverpool, UK) published in Local Economy: The Journal of the Local Economy Policy Unit Volume 34 Issue 6 (September 2019)
Abstract
In the aftermath of the UK’s vote to leave the European Union, a number of dominant narratives and spatial imaginaries of ‘Brexit’ have come to the fore including the notion of a revolt of a ‘Left Behind Britain’, and of a generational splintering manifested in different political attitudes.
Informed by this context, this paper considers some of these issues at the micro-scale, using voting data from two contiguous local authority districts within the same city region. It presents data from wards that have similar socio-economic conditions and which are highly ranked in the Index of Multiple Deprivation but which voted differently in the referendum.
The data reinforce the arguments of those who have claimed that the phenomenon of Brexit is powerfully contextual and that general socio-economic analyses of its causes do not fully explain why some areas and populations voted to leave the EU and others with comparable profiles voted to remain.
With poorer regions predicted to be the biggest economic losers of ‘Brexit’, an understanding of such issues is of material consequence and might inform progressive responses to such populist phenomena.
Showing posts with label referendum. Show all posts
Showing posts with label referendum. Show all posts
Tuesday, 10 December 2019
Tuesday, 24 July 2018
Parametrizing Brexit: mapping Twitter political space to parliamentary constituencies
an article by Marco Bastos and Dan Mercea (City, University of London, UK) published in Information, Communication & Society Volume 21 Issue 7 (2018)
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.
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.
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