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.

On June 23rd 2016, residents of the United Kingdom were asked whether the United Kingdom should remain a member of the European Union or leave.

This dataset contains the full results of this referendum for each local authority in London and the UK.

A blog analysing the results is also available.

Readers of The Guardian newspaper were the most likely to support remaining in the European Union, while readers of the Daily Express were the most in favor of leaving, according to a survey conducted among UK adults in 2019. The newspaper with the most divided readership was the Daily Mirror, which had a slight majority of it's readers support leaving the EU.

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 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).

Brexit and the Healthcare Industry Thematic Report OverviewIt has been three years since the UK departed from the EU. Even though Brexit was feared to impact UK’s economic growth at that time, Great Britain had Read More

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.

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.

How did population demographics impact the Brexit vote?

The corpus contains over 4.5 million tweets (tweet IDs) automatically labeled by a machine learning program with stance regarding Brexit: Positive (supporting Brexit), Negative (opposing Brexit), or Neutral (uncommitted).

The Brexit referendum was held on June 23, 2016, to decide whether the UK should leave or remain in the EU. In the weeks before the referendum, starting on May 12, the UK geo-located Brexit-related tweets were continuously collected resulting in a dataset of around 4.5 million (4,508,440) tweets from almost one million (998,054) users. A large sample of the collected tweets (35,000) was manually labeled for the stance of their authors regarding Brexit: Positive (supporting Brexit), Negative (opposing Brexit), or Neutral (uncommitted). The labeled tweets were used to train a classifier which then automatically labeled all the remaining tweets.

The corpus contains tweet ids and stance labels. The tweets are grouped into files one hour per file. In each file, one row represents one entry (twitter_id, sentiment_label). Lines are ordered by the tweet time.

The data collection, annotation, model training and performance estimation is described in detail in: Miha Grčar, Darko Cherepnalkoski, Igor Mozetič, Petra Kralj Novak: Stance and influence of Twitter users regarding the Brexit referendum. Computational Social Networks 4/6. 2017. http://dx.doi.org/10.1186/s40649-017-0042-6

The statistics of the Brexit referendum, as voted by British citizens, showing the number of people voting to either leave or remain in each of the four zones selected. **Motivation ** To exhibit our expertise to create a comprehensive solution for web-based interactive charts by using cutting-edge technologies.

Abstract copyright UK Data Service and data collection copyright owner.

The dataset consists of eight waves of survey data which were collected online by Kantar Public between July 2017 and August 2019, with one wave approximately every three months. There were 5300 respondents in wave 1, 3600 in wave 2, and around 3000 in each thereafter. After the first, each wave consists of a mix of return respondents from previous wave(s) and fresh 'top up' respondents. The aim was for each wave to be a representative sample of the UK, and as such efforts were made to recruit top-up respondents from groups with higher attrition rates (e.g. young Londoners). A cross-sectional weighting variable is provided for each wave. Respondents were selected from the 'Lightspeed panel'. On response to the invitation, respondents read about the aims of the study before actively opting in.
The eight surveys consist of a set of core questions on the topics of the Brexit negotiations, national identity, Brexit identity, political party identity interest in politics, most important issue, and political efficacy. There is a set of rotating topics including attitudes to immigration (various aspects), values, and social status. One-off topics include nostalgia, political knowledge, cultural capital, art preferences, experts, motivation for referendum vote, and more. Socio-demographic information was collected the first time the respondent took part in the study. We asked questions in every round including job status and subjective income, and asked about social class to all respondents in wave 5.
There is one file for each wave of data, but in addition, the data are provided in a single combined file in 'long format', where each respondent has a row of data for each wave in which they participated. Identifier and wave variables are provided.
Main Topics:

Public attitudes towards Brexit, the negotiations, and associated issues such as immigration, sovereignty, and knowledge of the EU. The data also include socio-demographic variables, and many other relevant topics such as cultural capital, nostalgia, trust, political efficacy, and national and party identity.

Brexit is significantly impacting consumers and retailers operating in the UK and the Europe in a myriad of ways, including trade tariffs, the movement of goods, changes in the labor market, and general repercussions relating to consumer attitudes and buying behavior a Read More

From September until December 2018, LSE Library held an exhibition of its archives relating to the history of the UK in and out of the EU called "What does Brexit mean to you?" The exhibition was free, unticketed and open to all, and housed right by the entrance to the Library. On one of the walls of the exhibition, the question "What does Brexit mean to you in three words?" was printed on a large poster, and visitors were invited to write their responses using post-it notes and stick them to the wall.This spreadsheet is a transcription of 664 of those post-it notes. Some changes to spelling/grammar have been made and changed into lowercase. The transcription was not conducted as a full-scale research project but rather as a record of audience participation in an exhibition, so this should be borne in mind when using this spreadsheet. There is no significance to the running number order - each post-it note was assigned a random number when being transcribed. It is possible one person filled out multiple post-it notes so it does not necessarily represent 664 individuals. The original post-it notes are held at LSE Library and are available for consultation there.

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.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Brexit: Everyone Loses, but Britain Loses the Most, PIIE Working Paper 19-5.

If you use the data, please cite as: Latorre, María C., Zoryana Olekseyuk, Hidemichi Yonezawa, and Sherman Robinson. (2019). Brexit: Everyone Loses, but Britain Loses the Most. PIIE Working Paper 19-5. Peterson Institute for International Economics.

This documentary archive was created as part of the Brexit priority grant, The Repatriation of Competences: Implications for devolution. It is currently being expanded as part of the ESRC large grant, Between Two Unions (ES/P009441/1). At the time of submission, it is complete up to July 2019. The archive is composed of documents including political speeches, government consultations and policy reports, parliamentary debates and reports, and court judgments. All documents are in the public domain, but the archive collated those most relevant to scholars of devolution, and compiled them in a searchable wiki. The wiki is

The devolution settlements in the United Kingdom have been embedded in UK membership of the European Union. Policy areas like agriculture, the environment, fisheries, regional development and justice and home affairs, are both matters for the devolved parliaments and also areas that fall under the authority of the EU. In these policy fields, the EU has provided a common framework that has limited the degree of difference that has emerged within and across the UK, and this has helped keep the nations of the UK together. Whichever model is reached after negotiations, the UK's withdrawal from the EU will affect the powers of the devolved nations in complex ways. It may lead to further decentralisation of power to the devolved institutions. Alternatively, it could lead to powers being recentralized within UK-wide institutions. A third possibility is that it could see the setting up of new forums and process to enable the UK Government and the devolved governments to cooperate more closely on policy areas where their powers overlap. The outcome of the negotiations, and the decisions taken by key actors, will have consequences for the powers and responsibilities of institutions in the devolved nations and their relationships with the rest of the UK. This project will carry out a study of these developments as the Brexit negotiations get underway, and we will examine how the outcomes will shape devolution and relations between the UK's four governments. We will study and support the role of parliaments in scrutinizing the Brexit negotiation processes and the outcomes. We will meet with civil servants to help them understand the effects of different Brexit options on devolution in Scotland, Northern Ireland and Wales. Our project will focus on three particular areas of devolved policy - agriculture, the environment and justice and home affairs - to investigate the particular consequences of bringing powers back from the EU to the UK after Brexit. We will work with professionals and representatives from these sectors to help them prepare and plan for Brexit under different scenarios. We will also enhance broader understanding of the process, outcomes and their impact on devolution by producing easy to read and easy to access explanations, analyses and reports, and by taking up opportunities for commenting in the media.

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.