This archive contains shared materials pertaining to the forthcoming paper "Local media and geo-situated responses to Brexit: A quantitative analysis of Twitter, news and survey data" by Genevieve Gorrell, Mehmet E. Bakir, Luke Temple, Diana Maynard, Jackie Harrison, J. Miguel Kanai and Kalina Bontcheva.It contains a folder with a separate document for each of the topic-model-derived topics explored in the paper. The first two columns are topic scores for material from each separate Twitter account in the corpus, along with their Brexit vote intention. After a blank column comes the national newspaper article topic scores. After a further blank column come the local newspaper article scores, along with the NUTS1 region in which they are published.Additionally there is a spreadsheet with entity-based topic scores for each newspaper.Ethics approval was obtained for the Twitter data collection from the University of Sheffield (application number 011934).

As of June 2025, 56 percent of people in Great Britain thought that it was wrong to leave the European Union, compared with 31 percent who thought it was the right decision. During this time period, the share of people who regret Brexit has been slightly higher than those who support it, except for some polls in Spring 2021, which showed higher levels of support for Brexit. Is Bregret setting in? Since late July 2022, the share of people who regret Brexit in these surveys has consistently been above 50 percent. Additionally, a survey from January 2025 highlighted that most people in the UK thought that Brexit had had a mainly negative impact, especially on the cost of living and the economy. Despite there being a clear majority of voters who now regret Brexit, there is as yet no particular future relationship with the EU that has overwhelming support. As of late 2023, 31 percent of Britons wanted to rejoin the EU, while 30 percent merely wanted to improve trade relations and not rejoin either the EU or the single market. Leave victory in 2016 defied the polls In the actual referendum, which took place on June 23, 2016, Leave won 51.9 percent of the votes and Remain 48.1 percent, after several polls in the run-up to the referendum put Remain slightly ahead. Remain were anticipated to win until early results from North East England suggested that Leave had performed far better than expected, with this pattern replicated throughout the country. This event was repeated somewhat in the U.S. election of that year, which saw Donald Trump win several key swing states such as Pennsylvania and Wisconsin, despite predictions that these states would vote for Hillary Clinton.

This submission contains the dataset (Stata and SPSS versions) and survey questionnaire for the 2016 study of public opinion on Brexit.

People in the United Kingdom who worked in the engineering industry were the most likely to be in favor of the the UK leaving the European Union, according to a survey conducted among UK adults in 2019. By contrast, over three quarters of people who said they worked in design wanted the UK to stay in the EU.

The outcome of the British referendum on European Union (EU) membership sent shockwaves through Europe. While Britain is an outlier when it comes to the strength of Euroscepticism, the anti-immigration and anti-establishment sentiments that produced the referendum outcome are gaining strength across Europe. Analysing campaign and survey data, this article shows that the divide between winners and losers of globalization was a key driver of the vote. Favouring British EU exit, or ‘Brexit’, was particularly common among less-educated, poorer and older voters, and those who expressed concerns about immigration and multi-culturalism. While there is no evidence of a short-term contagion effect with similar membership referendums in other countries, the Brexit vote nonetheless poses a serious challenge to the political establishment across Europe.

To further our understanding of public reaction to political movement via social media, this dataset provides 17998 unfiltered tweets taken on the morning that Brexit was announced. This dataset contains metadata such as geolocation as an independent variable, to allow for rigorous qualitative investigation to be used.

Additional tweets from trending topics were also taken at the same time provide context on trending themes at the time: - Scotland - Jeramy Corbyn - Nichola Sturgeon - David Cameron - EURefResults - Euromillions - Borris

Data was captured with NCapture from QSR.

Replication Material

Dataset

This dataset was created by Scar

Released under CC0: Public Domain

Contents

The 2016 EU referendum in Britain gave rise to two new political identities which divided the electorate into ‘Remainers’ and ‘Leavers’. Yet little is known about how these new identities relate to policy attitudes. The literature on partisanship identifies policy group norms that allow partisans to settle on shared policy aims and provide cues about the ‘correct’ in-group policy choice. In this paper, we examine the extent to which these policy norms are also important for people with a Brexit identity. We show that Brexit identities are associated with specific policy preferences and that these group norms are relatively well-known to people. Using a survey-embedded experiment, we also demonstrate that providing policy cues to Brexit identity groups may encourage people to align their own preferences with their group’s preferences. These findings contribute to the growing literature on non-partisan political identities and their importance in shaping political attitudes and behavior.

In the Brexit referendum of 2016, 59.3 percent of voters in the West Midlands voted to leave the European Union, the most of any region. By contrast, 62 percent of voters in Scotland voted to remain in the European Union with only 38 percent voting to leave. Overall, 17.4 million people voted to Leave the European Union in 2016, compared with 16.1 million who voted Remain, or 51.9 percent of the vote to 48.1 percent.

This paper develops an empirical agent-based model to assess the impacts of Brexit on Scottish cattle farms. We first identify several trends and processes among Scottish cattle farms that were ongoing before Brexit: the lack of succession, the rise of leisure farming, the trend to diversify and industrialise, and, finally, the phenomenon of the “disappearing middle”, characterised by the decline of medium-sized farms and the polarization of farm sizes. We then study the potential impact of Brexit amid the local context and those ongoing social processes. We find that the impact of Brexit is indeed subject to pre-Brexit conditions. For example, whether industrialization is present locally can significantly alter the impact of Brexit. The impact of Brexit also varies by location: we find a clear divide between constituencies in the north (highland and islands), the middle (the central belt) and the south. Finally, we argue that policy analysis of Brexit should consider the heterogeneous social context and the complex social processes under which Brexit occurs. Rather than fitting the world into simple system models and ignoring the evidence when it does not fit, we need to develop policy analysis frameworks that can incorporate real world complexities, so that we can assess the impacts of major events and policy changes in a more meaningful way.

This is an Excel spreadsheet file containing an archive of 1,100 @vote_leave Tweets publicly published by the queried account between 12/06/2016 09:06:22 - 21/06/2016 09:29:29 BST.The spreadsheet contains four more sheets containing a data summary from the archive, a table of tweets' sources, and tables of corpus term and trend counts and collocate counts.The Tweets contained in the Archive sheet were collected using Martin Hawksey's TAGS 6.0. The profile_image_url column has been removed.The text analysis was performed using Stéfan Sinclair's & Geoffrey Rockwell's Voyant Tools (c 2016).The data is shared as is. The sharing of this dataset complies with Twitter's Developer Rules of the Road.Please note that both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (Gonzalez-Bailon, Sandra, et al. 2012). Therefore it cannot be guaranteed this file contains each and every Tweet actually published by the queried Twitter account during the indicated period, and is shared for comparative and indicative educational research purposes only.Only content from public accounts is included and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.Each Tweet and its contents were published openly on the Web, they were explicitly meant for public consumption and distribution and are responsibility of the original authors. Any copyright belongs to its original authors.No Personally identifiable information (PII), nor Sensitive Personal Information (SPI) was collected nor is contained in this dataset.This dataset is shared as a sample and as an act of citizen scholarship in order to archive, document and encourage open educational and historical research and analysis.

These datasets were collated as part of a Master's dissertation project. This Twitter dataset covers the January - March 2022 period, and is comprised of tweets relating to Brexit or Europe from Twitter accounts with publicly stated Brexit positions in their bio. The Boolean search used to create the pro-Brexit dataset is here: (bio:"Brexit support*" OR bio:"pro-brexit" OR bio:"pro brexit" OR bio:"Pro #Brexit" OR bio:brexiteer OR bio:probrexit) AND (EU OR Brexit* OR CUSTOMS OR EUROPEAN OR EUROPE OR #Remain OR Brexit OR #rejoinEU) The Boolean search used to create the pro-Brexit dataset is here: (bio:"anti brexit" OR bio:"anti-brexit" OR bio:"antibrexit" OR bio:"Pro remain" OR bio:"pro-remain" OR bio:remainer) AND (EU OR BREXIT* OR CUSTOMS OR EUROPEAN OR EUROPE OR #Remain OR *Brexit) A wide range of data relating to the tweets are in this dataset, including: Field name Field description Date The date the tweet was posted to Twitter. URL URL link to the tweet Hit Sentence Tweet text Influencer Account name for the user who shared the tweet Country Country location of the Twitter account (if declared) Subregion Locale of the Twitter account (if declared) Language Automated language detection Reach Estimate of the “reach” of the tweet, based on followers at the time of sending tweet Engagement Sum of public engagements for the tweet (sum of likes, retweets and quote tweets) Twitter Screen Name Displayed name as appears on the Twitter profile of account User Profile Url URL link to the Twitter account that shared the tweet Twitter Bio Text from the Twitter bio section of the account Twitter Followers Number of followers of the account Twitter Following Number of accounts that are followed by this account Alternate Date Format The date the tweet was posted to Twitter. Time The time the tweet was posted to Twitter.

This map displays where people either voted to leave or remain in the European Union. Click on an area to view the percentage and count of people who voted on either side as well as the turnout. Other fields included in this dataset are the number of electorates and the declaration time.

A large dataset containing the public tweets about Brexit comprising 45 months (from January 2016 until September 2019). This dataset comprises 50.8 million tweets and 3.97 million users. It also contains additional attributes including political stance classification, sentiment analysis, and automated account (bot) scores.

SSIX BREXIT Gold Standard

This repository contains the BREXIT Twitter Gold Standard produced by the SSIX Project https://ssix-project.eu/.

Only a sample is available here, to rebuild the full dataset, follow the instructions on the SSIX Project code repository:

https://bitbucket.org/ssix-project/brexit-gold-standard

Brexit – the term used to refer to the UK’s planned departure from the European Union (EU) – is one of the most important and controversial political stories of recent times. Technology companies worldwide need to reformulate their strategies and progress action plans in preparation for Brexit. The rejection by Members of Parliament (MPs) of British prime minister Theresa May’s Brexit deal has left the technology industry staring at a ‘No Deal’ scenario, with potentially severe impacts on the ability of tech companies to service contracts with customers and worries over falling investment and failing competitiveness because of the ongoing political and economic uncertainty. Read More

Combined dataset derived from various government sources (see below), allowing comparison of Brexit / EU referendum voting patterns by local authority and socio-economic demographic data. Contains data for Great Britain (England, Scotland, and Wales).

Original datasets:

EU Referendum Results - Electoral Commission

Rural Urban Classification (2011) of Local Authority Districts in England - Office for National Statistics

Population of the UK by country of birth and nationality (July 2015 to June 2016) - Office for National Statistics

2011 Census: KS201UK Ethnic group, local authorities in the United Kingdom - The National Archives

CC01 Regional labour market: Claimant Count by unitary and local authority (experimental) (20 July 2016) - Office for National Statistics

Regional GVA(I) by local authority in the UK - Office for National Statistics

Estimates of the population for the UK, England and Wales, Scotland and Northern Ireland (Mid-2016: Superseded) - Office for National Statistics

2011 Census: KS501UK Qualifications and students, local authorities in the United Kingdom - The National Archives

We estimate the impact of increased policy uncertainty from Brexit on UK trade inservices. We apply an uncertainty-augmented gravity equation to UK services tradewith the European Union at the industry level from 2016Q1 to 2018Q4. By exploitingthe variation in the probability of Brexit from prediction markets interacted with anew trade policy risk measure across service industries we identify a significant negativeimpact of the threat of Brexit on trade values and participation. The increasedprobability of Brexit in this period lowered services exports by at least 20 log points.