The UK 2015 General Election, Twitter data

Chukwuemeka , David and Abdul, Adeniyi (2020). The UK 2015 General Election, Twitter data. [Data Collection]. Colchester, Essex: UK Data Archive. 10.5255/UKDA-SN-852772

There is now a broad consensus that new forms of social data emerging from people’s day-to-day activities on the web have the potential to transform the social sciences. However, there is also agreement that current analytical techniques fall short of the methodological standards required for academic research and policymaking and that conclusions drawn from social media data have much greater utility when combined with results drawn from other datasets (including various public sector resources made available through open data initiatives).

In this proposal we outline the case for further investigations into the challenges surrounding social media data and the social sciences. Aspects of the work will involve analysis of social media data in a number of contexts, including: (1) transport disruption around the 2014 Commonwealth Games (Glasgow); (2) news stories about Scottish independence and UK-EU relations; (3) Island communities in the Western Isles.
Guided by insights from these case studies we will:
(1) develop a suite of software tools to support various aspects of data analysis and curation, (2) provide guidance on ethical considerations surrounding analysis of social media data, (3) deliver training workshops for social science researchers and (4) engage with the public on this important topic through a series of festivals (food, music, science).

Data description (abstract)

This archive includes files containing the IDs of Tweets collected during the 2015 General Election for the project Social Media - Developing Understanding, Infrastructure & Engagement (Social Media Enhancement), using standard Twitter API.

Data creators:

Creator Name	Affiliation	ORCID (as URL)
Chukwuemeka David	University of Aberdeen
Abdul Adeniyi	University of Aberdeen

Sponsors:

Economic and Social Research Council

Grant reference:

ES/M001628/1

Topic classification:

Politics
Economics
Labour and employment

Keywords:

political campaigns, social media, Twitter, UK General Election

Project title:

Social Media - Developing Understanding, Infrastructure and Engagement (Social Media Enhancement)

Grant holders:

Peter Edwards, Caitlin Cottrill, Adam Wyner, John Bone, George Coghill, Timothy Norman, Claire Wallace, John Nelson, Wei Pang, Roger Maull

Project dates:

From	To
14 February 2014	31 August 2016

Date published:

12 Dec 2017 18:30

Last modified:

12 May 2020 16:40

Coverage and Methodology

Collection period:

Date from:	Date to:
6 May 2015	11 May 2015

Country:

United Kingdom

Data collection method:

The Twitter data were collected by Dr. Chukwuemeka David Emele and Mr. Adeniyi Abdul when they worked at the University of Aberdeen.
We use standard Twitter API to collect the data.
The following keywords are the samples of the hashtags used for collecting the above Tweets: Westminister, Cameron, Miliband,Sturgeon, Clegg, Farage, UKIP, Plaid Cymru, Libdem, Liberal, Communist, Labour, Labor, Conservative, Tories, Liberal Democrat, SNP, Green, left wing, right wing, left-wing, right wing, employment, unemployment, work, jobs, wages, pay, salaries, low pay, parttime, rich, housing, second homes, banks, banking, bankers, economy, crisis, recession, recovery, bailout, interest rates, socialist, socialism, redtories, neoliberalism, globalisation, globalization, consumption, protest, budget, business, consumer, benefits, scroungers, skivers, hard working, immigration, emigration, pension, pensioner, public service, debt, public spending, welfare, zero hours, insecurity, insecure work, tax avoidance, tax evasion, inequality, taxation, tax, BTL, buy to let, mortgages, rents, renting, housing prices, energy, oil, privatisation, NHS, health, cuts, Welsh, Irish, English, Scots, Scottish, Ireland, Wales, England, Scotland, nationalism, independence, austerity, deficit.

Observation unit:

Individual, Organization, Event/Process, Group, Object

Kind of data:

Other

Type of data:

Historical data

Resource language:

English

Access and Administration