As of February 2025, approximately 34.2 percent of X (formerly Twitter) users in the United Kingdom (UK) were women. By comparison, male users on the social network accounted for 65.8 percent of total users.

The number of Twitter users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.9 million users (+5.1 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 18.55 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

As of September 2024, Millennials, those born between 1980 and 1994, made up ** percent of X's (formerly Twitter) user base in the United Kingdom. Overall, ** percent of users in the UK belonged to Generation X, and ** percent were of Generation Z.

The platform is male-dominated with 68.1% of all Twitter users being male. Just 31.9% of Twitter users are female.

As of September 2024, ** percent of X (formerly Twitter) users in the United Kingdom were from a high income household, while ** percent were from a medium income household.

The US has historically been the target country for Twitter since its launch in 2006. This is the full breakdown of Twitter users by country.

As of **************, ** percent of X (formerly Twitter) users in the United Kingdom held a Bachelor's degree or equivalent qualification, while ** percent held a master’s degree. Overall, just ***** percent of X users were of a doctoral degree level of education.

This is the breakdown of Twitter users by age group.

These Twitter user statistics will give you the complete story of where Twitter is at today and what the future looks like for the social media company.

Social network X/Twitter is particularly popular in the United States, and as of February 2025, the microblogging service had an audience reach of 103.9 million users in the country. Japan and the India were ranked second and third with more than 70 million and 25 million users respectively. Global Twitter usage As of the second quarter of 2021, X/Twitter had 206 million monetizable daily active users worldwide. The most-followed Twitter accounts include figures such as Elon Musk, Justin Bieber and former U.S. president Barack Obama. X/Twitter and politics X/Twitter has become an increasingly relevant tool in domestic and international politics. The platform has become a way to promote policies and interact with citizens and other officials, and most world leaders and foreign ministries have an official Twitter account. Former U.S. president Donald Trump used to be a prolific Twitter user before the platform permanently suspended his account in January 2021. During an August 2018 survey, 61 percent of respondents stated that Trump's use of Twitter as President of the United States was inappropriate.

These are the key Twitter user statistics that you need to know.

This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: http://www.cosmosproject.net/) relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect “signatures” of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed.

Social media platforms are integral to people's lives, offering ways to communicate, create and view content and share information. According to Ofcom, approximately 89% of UK internet users in 2023 used social media apps or sites. Teenagers and young adults are the biggest users, although there is rapid uptake among older age groups. Advertising is the primary revenue source for social media platforms, although subscription-based services are gaining momentum as platforms seek to diversify their incomes. TikTok is the success story of the last few years, becoming the most downloaded app between 2020 and 2022, according to Apptopia. The short-form video platform reported that it averaged revenue growth of over 450% between 2019 and 2022. After Musk's takeover, X, formerly known as Twitter, adjusted its content moderation and allowed previously banned accounts to return. As a result, over 600 advertisers have pulled their ads from the site because of fears their brand may be associated with malcontent. In response to falling ad revenue, X has introduced a subscription-based service which enables users to verify themselves and boosts the number of people who view their tweets. Meta-owned Facebook and Instagram have responded by introducing a similar service. Revenue is expected to grow by 14.3% in 2024-25, constrained by a slowdown in user growth for most major social media platforms. Over the five years through 2024-25, revenue is forecast to expand at a compound annual rate of 32.8% to reach £9.8 billion. Looking forward, regulations relating to how data is collected, stored, and shared will force advertisers and platforms to rethink how they can target their desired demographics. The rising prominence of AI will require the introduction of adequate regulations. The Online Safety Bill sets out new guidelines for social media platforms to abide by, with hefty fines in store for those who do not. Operating costs will swell as platforms look to meet consumers’ expectations, weighing on profit. Over the five years through 2029-30, social media platforms' revenue is projected to climb at an estimated 9.4% to reach £15.4 billion.

This dataset contains ~11M tweets related to the 2019 United Kingdom General Election, published and collected between November 12, 2019, and December 12, 2019. In addition, we provide nodes and edges of the superspreader user similarity network, as described in the paper below.

Please, refer to the paper below for more details.

Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., & Tesconi, M. (2021). Coordinated Behavior on Social Media in 2019 UK General Election. In 15th International Conference on Web and Social Media. AAAI.

In detail, the dataset consists of:

tweet-ids.csv.zip: a CSV file with the column "tweet_id," containing 11,264,820 tweet IDs related to the 2019 United Kingdom General Election, published and collected between November 12, 2019, and December 12, 2019.

superspreader-nodes.csv.zip: a CSV file with the columns "id" and "cluster," relating to the nodes of the superspreader user similarity network mentioned in the paper.

superspreader-edges.csv.zip: a CSV file with the columns "source," "target," and "weight," relating to the edges of the superspreader user similarity network mentioned in the paper.

Twitter is ranked as the 12h most popular social media site in the world. The platform currently has 611 million active monthly users.

This is the Twitter Parliamentarian Database: a database consisting of parliamentarian names, parties and twitter ids from the following countries: Austria, Belgium, France, Denmark, Spain, Finland, Germany, Greece, Italy, Malta, Poland, Netherlands, United Kingdom, Ireland, Sweden, New Zealand, Turkey, United States, Canada, Australia, Iceland, Norway, Switzerland, Luxembourg, Latvia and Slovenia. In addition, the database includes the European Parliament.The tweet ids from the politicans' tweets have been collected from September 2017 - 31 October 2019 (all_tweet_ids.csv). In compliance with Twitter's policy, we only store tweet ids, which can be re-hydrated into full tweets using existing tools. More information on how to use the database can be found in the readme.txt.It is recommended that you use the .csv files to work with the data, rather than the SQL tables. Information on the relations in the SQL database can be found in the Database codebook.pdf.Update:The tweet ids for 2021 have been added as '2021.csv'Update #2:The tweet ids for 2020 have been added as '2020.csv'The last party table has been added as 'parties_2021_04_28.csv'The last members table has been added as 'members_2021_04_28.csv'

As of July 2024, X's (formerly Twitter) monthly social network market share in the United Kingdom was 10.49 percent. In April 2020, X monthly social network market share in the UK was around 37 percent, an all time high for the micro-blogging platform. Furthermore, X emerged as the second leading social media website in the UK.

Data analysed in Alis et al., "Quantifying regional differences in the length of Twitter messages" Fields ------tweet id: retrieve tweet by passing this id to the REST APImlen: length of message, in characterswlen: length of message, in wordsmratio: proportion of message

Breakdown of Twitter users by age groups.

Description

The MIGR-TWIT Corpus is a multilingual corpus of tweets about the topic of migration in Europe. Within the framework of the collaborative research project OLiNDiNUM (Observatoire LINguistique du DIscours NUMérique, Linguistic Observatory of Online Debate) the MIGR-TWIT Corpus is created with the aim of developing language databases of online debate. Considering the global issue of migration in line with British and French political contexts of last dozen years from 2011 to 2022, the corpus consists of two sub-corpora:

• FR-R-MIGR-TWIT-2011-2022 Corpus for French language data (1 January 2011 - 30 June 2022) and

• UK-R-MIGR-RA-TWIT-2012-2022 Corpus for English language data (1 January 2012 - 5 September 2022)

Using the Twitter API v2 Academic Research, tweets containing at least one occurrence of migration or refugee related words are retrieved automatically from 28 right and far-right political figures and parties. The whole corpus contains 18,233 tweets and 533,198 words.

Scientific reference:

Pietrandrea, P., Battaglia, E. (2022). “Migrants and the EU”. The diachronic construction of ad hoc categories in French far-right discourse. Journal of Pragmatics 192, 139-157.

Blandino, G. (2023). 10 years of public debate on immigration: combining topic modeling and corpus linguistics to examine the British (far-)right discourse on Twitter, MA University of Wolverhampton

Jeon, S. (2025). Le discours numérique sur l'immigration en France entre 2011 et 2022. Une analyse de corpus (Online Discourse on Immigration in France between 2011 and 2022. A Corpus Analysis), PhD Thesis, Université de Lille, France.

Contents

The whole corpus contains two CSV Zip files (tabular format) corresponding to each sub-corpus. The complete corpus is presented in two versions, one version with the tweet identifier (data_id) and the text of the tweet (data_text) as a header (folders named FR-R-MIGR-TWIT-2011-2022_textonly and UK-R-MIGR-RA-TWIT-2012-2022_textonly, respectively composed of 12 and 11 Zip files of every single year), and the other version with all tweet fields information included as a header, such as the posting date (data_created_at), the username (author_name), the number of retweets (data_public_metrics_retweet_count), etc., with two folders named FR-R-MIGR-TWIT-2011-2022_meta and UK-R-MIGR-RA-TWIT-2012-2022_meta. Detailed information for each sub-corpus is illustrated below.

1. FR-R-MIGR-TWIT-2011-2022

• Created at: 2022-08-08

• Language: FR

• Coverage: 16 user accounts; 11,761 tweets; 358,491 words

• Time of data collection: start=2011-01-01; end=2022-06-30

• Keywords: words derived from a latin root “migr” of migrare

• Corpus composition:

	Political figure/party	Username	Tweets	Year concerned
1	Michel Barnier	@MichelBarnier	31	2017-22
2	Valérie Pécresse	@vpecresse	81	2017-22
3	Rassemblement National	@RNational_off	3,347	2017-22
4	Nicolas Dupont-aignan	@dupontaignan	663	2011-22
5	Éric Ciotti	@ECiotti	1,007	2012-22
6	Christian Estrosi	@cestrosi	137	2011-22
7	Marine Le Pen	@MLP_officiel	1,650	2011-22
8	Valérie Boyer	@valerieboyer13	837	2012-22
9	Florian Philippot	@f_philippot	485	2012-22
10	Xavier Bertrand	@xavierbertrand	70	2017-22
11	Marion Maréchal	@MarionMarechal	479	2012-17,19-22
12	Philippe Meunier	@Meunier_Ph	245	2013-22
13	Jordan Bardella	@J_Bardella	1,095	2013-22
14	Nicolas Bay	@NicolasBay_	1,260	2017-22
15	Emmanuel Macron	@EmmanuelMacron	72	2017-22
16	Éric Zemmour	@ZemmourEric	302	2019-22
17	Jean Messiha*	Banned from Twitter (since July 2021)	-	-

• Political figures and parties of table above are listed in chronological order according to the dates on which they posted their first tweet.

• *Before the launching of Twitter API v2 Academic Research, migr-tweets were collected from the database of Europresse.com including 1,453 tweets of Jean Messiha as part of the reference study (Pietrandrea & Battaglia 2022). However, the Twitter account in question has been permanently banned since July 2021. For our data collection using the Twitter API started in September 2021, we could not access this account. Therefore, we decided not to include his tweets in the FR-R-MIGR-TWIT-2011-2022 for the sake of consistency with the rest of twitter data that are automatically retrieved.

• The sub-corpus FR-R-MIGR-TWIT-2017-2022 is developed, annotated and analyzed as part of a doctoral thesis in progress (Jeon, 2025) with the aim of studying the semantic construction of migr-lexicon over the period between 2011 and 2022.

2. UK-R-MIGR-RA-TWIT-2012-2022

• Created at: 2022-09-06

• Language: EN

• Coverage: 12 user accounts; 6,472 tweets; 174,707 words

• Time of data collection: start=2012-01-01; end=2022-09-05

• Keywords: words derived from a latin root “migr” of migrare in addition to the keywords “refugee(s)” and “asylum”.

• Corpus composition:

	Political figure/party	Username	Tweets	Year concerned
1	David Cameron	@David_Cameron	32	2012-22
2	Amber Rudd	@AmberRuddUK	29	2012-22
3	Sajid Javid	@sajidjavid	84	2012-22
4	Boris johnson	@BorisJohnson	80	2015-22
5	Priti Patel	@pritipatel	304	2012-22
6	UK Home Office	@ukhomeoffice	909	2012-22
7	Nigel Farage	@Nigel_Farage	1,010	2012-22
8	Richard Tice	@TiceRichard	180	2013-22
9	UKIP	@UKIP	2,746	2012-22
10	Neil Hamilton	@NeilUKIP	252	2013-22
11	Nick Griffin	@NickGriffinBU	542	2012-22
12	Robin Tilbrook	@RobinTilbrook	304	2012-22

• 2 out of 12 accounts are official accounts belonging to the” UK Home Office” department and the “UKIP” (United Kingdom Independence Party) party. 10 out of 12 accounts are political figures’ accounts.

• The corpus UK-R-MIGR-RA-TWIT-2012-2022 will be exploited for the following master’s thesis: Blandino, G. (2023). 10 years of public debate on immigration: combining topic modeling and corpus linguistics to examine the British (far-)right discourse on Twitter, MA University of Wolverhampton.