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

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.

As of February 2025, 37.5 percent of X’s (formerly Twitter) global audience was aged between 25 and 34 years. The second-largest age group demographic on the platform was represented by users aged between 18 and 24 years, with a share of 32.1 percent. Users aged less than 18 years accounted for two percent of users, while those aged 50 or older accounted for roughly 7.3 percent. X is a male-dominated platform As of January 2024, more than 60 percent of X users were male. Although all mainstream social media platforms tend to have a slightly more male-skewing audience, X stands out above Instagram, Snapchat, TikTok, and Facebook when it comes to user gender demographics. Overall, Pinterest is the only mainstream platform to have a higher share of female users. X Blue for you It is not uncommon for social media users to now have the chance to become subscribers of their chosen online networks for a monthly fee. X Blue is a subscription service from X that gives users special benefits and features. A blue verification mark, edit post functionality, fewer ads, priority ranking in chats, and longer video upload times are some of the perks offered.

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.

The purpose of this data collection was to test a scale for detecting verbal violence in Tweets. Workers at Mechanical Turk were first asked to complete a qualification test and then invited to code additional Tweets according to our scale. The qualification test involved a detailed explanation of each item of the scale, a walkthrough of a tweet that we had coded according to all 14 scale-items, a practice exercise, and a test. In the practice exercise, potential coders attempted to code a tweet on their own using our scale. After submitting their ratings, they were shown our own ratings for the same tweet and explanations for each of our ratings. The test component consisted of another coding task, in which coders were asked to code another tweet that we had already coded ourselves. The workers who, on test, with our ratings of that tweet on at least 11 out of the 14 items “passed” the test, earning the qualification that allowed them to participate in future coding tasks. Variables in the data include the ID of the Tweet (so that you may find it on Twitter; Twitter Terms of Service prohibit us from sharing the Tweets), the ID number we assigned to the coder, the rating that coder provided for each of the 14 items on our scale, the gender and age of the coder, and any comments the coder provided.

Author: Víctor Yeste. Universitat Politècnica de Valencia.This work is an exploratory, quantitative, and not experimental study with an inductive inference type and a longitudinal follow-up. It analyzes movie data and tweets published by users using the official Twitter hashtags of movie premieres the week before, the same week, and the week after each release date.The scope of the study is the collection of movies released in February 2022 in the USA, and the object of the study includes them and the tweets that refer to the film in the 3 closest weeks to their premiere dates. The tweets recollected were classified by the week they were published, so they are classified by a time dimension called timepoint. The week before the release date has been designated as timepoint 1, the week of the release date is timepoint 2, and the week immediately afterward is timepoint 3. Another dimension that has been considered is if the movie has domestic production or not, which means that if one of the countries of origin is the United States, the movie is designated as domestic.The chosen variables are organized in two data tables, one for the movies and one for the collected tweets.Variables related to the movies:id: Internal id of the moviename: Title of the moviehashtag: Official hashtag of the moviecountries: List of countries of the movie, separated by a semicolonmpaa: Film ratings system by the Motion Picture Association of America. It is a completely voluntary rating system and ratings have no legal standing. The currently rating systems include G (general audiences), PG (parental guidance suggested), PG-13 (parents strongly cautioned), R (restricted, under 17 requires accompanying parent or adult guardian) and NC-17 (no one 17 and under admitted)(Film Ratings - Motion Picture Association, n.d.)genres: List of genres of the movie, e.g., Action or Thriller, separated by a semicolonrelease_date: Release date of the movie in a format YYYY-MM-DDopening_grosses: Amount of USA dollars that the movie obtained on the opening date (the first week after the release date)opening_theaters: Amount of USA theaters that released the movie on the opening date (the first week after the release date)rating_avg: Average rating of the movieVariables related to the tweets:id: Internal id of the tweetstatus_id: Twitter id of the tweetmovie_id: Internal id of the movietimepoint: Week number related to the movie premiere that the tweet was published on. “1” is the week before the movie release, “2” is the week after the movie release” and “3” is the second week after the movie release.author_id: Twitter id of the author of the tweetcreated_at: Date and time of the tweet, with format “YYYY-MM-DD HH:MM:SS”quote_count: Number of the tweet’s quotesreply_count: Number of the tweet’s repliesretweet_count: Number of the tweet’s retweetslike_count: Number of the tweet’s likessentiment: Sentiment analysis of the tweet’s content with a range from -1 (negative) to 1 (positive)This dataset has contributed to the elaboration of the book chapters:Yeste, Víctor; Calduch-Losa, Ángeles (2022). Genre classification of movie releases in the USA: Exploring data with Twitter hashtags. In Narrativas emergentes para la comunicación digital (pp. 1012-1044). Dykinson, S. L.Yeste, Víctor; Calduch-Losa, Ángeles (2022). Exploratory Twitter hashtag analysis of movie premieres in the USA. In Desafíos audiovisuales de la tecnología y los contenidos en la cultura digital (pp. 169-187). McGraw-Hill Interamericana de España S.L.Yeste, Víctor; Calduch-Losa, Ángeles (2022). ANOVA to study movie premieres in the USA and online conversation on Twitter. The case of rating average using data from official Twitter hashtags. In El mapa y la brújula. Navegando por las metodologías de investigación en comunicación (pp. 151-168). Editorial Fragua.

As of December 2022, X/Twitter's audience accounted for over *** million monthly active users worldwide. This figure was projected to ******** to approximately *** million by 2024, a ******* of around **** percent compared to 2022.

This is the breakdown of Twitter users by age group.

TweetNERD - End to End Entity Linking Benchmark for Tweets

Paper - Video - Neurips Page

This is the dataset described in the paper TweetNERD - End to End Entity Linking Benchmark for Tweets (accepted to Thirty-sixth Conference on Neural Information Processing Systems (Neurips) Datasets and Benchmarks Track).

Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (NLP) applications. We introduce TweetNERD, a dataset of 340K+ Tweets across 2010-2021, for benchmarking NERD systems on Tweets. This is the largest and most temporally diverse open sourced dataset benchmark for NERD on Tweets and can be used to facilitate research in this area.

TweetNERD dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0) LICENSE.

The license only applies to the data files present in this dataset. See Data usage policy below.

Check out more details at https://github.com/twitter-research/TweetNERD

Usage

We provide the dataset split across the following tab seperated files:

• OOD.public.tsv: OOD split of the data in the paper.
• Academic.public.tsv: Academic split of the data described in the paper.
• part_*.public.tsv: Remaining data split into parts in no particular order.

Each file is tab separated and has has the following format:

tweet_id	phrase	start	end	entityId	score
22	twttr	20	25	Q918	3
21	twttr	20	25	Q918	3
1457198399032287235	Diwali	30	38	Q10244	3
1232456079247736833	NO_PHRASE	-1	-1	NO_ENTITY	-1

For tweets which don't have any entity, their column values for phrase, start, end, entityId, score are set NO_PHRASE, -1, -1, NO_ENTITY, -1 respectively.

Description of file columns is as follows:

Column	Type	Missing Value	Description
tweet_id	string		ID of the Tweet
phrase	string	NO_PHRASE	entity phrase
start	int	-1	start offset of the phrase in text using UTF-16BE encoding
end	int	-1	end offset of the phrase in the text using UTF-16BE encoding
entityId	string	NO_ENTITY	Entity ID. If not missing can be NOT FOUND, AMBIGUOUS, or Wikidata ID of format Q{numbers}, e.g. Q918
score	int	-1	Number of annotators who agreed on the phrase, start, end, entityId information

In order to use the dataset you need to utilize the tweet_id column and get the Tweet text using the Twitter API (See Data usage policy section below).

Data stats

Split	Number of Rows	Number unique tweets
OOD	34102	25000
Academic	51685	30119
part_0	11830	10000
part_1	35681	25799
part_2	34256	25000
part_3	36478	25000
part_4	37518	24999
part_5	36626	25000
part_6	34001	24984
part_7	34125	24981
part_8	32556	25000
part_9	32657	25000
part_10	32442	25000
part_11	32033	24972

Data usage policy

Use of this dataset is subject to you obtaining lawful access to the Twitter API, which requires you to agree to the Developer Terms Policies and Agreements.

Please cite the following if you use TweetNERD in your paper:

@dataset{TweetNERD_Zenodo_2022_6617192,
 author    = {Mishra, Shubhanshu and
         Saini, Aman and
         Makki, Raheleh and
         Mehta, Sneha and
         Haghighi, Aria and
         Mollahosseini, Ali},
 title    = {{TweetNERD - End to End Entity Linking Benchmark 
          for Tweets}},
 month    = jun,
 year     = 2022,
 note     = {{Data usage policy Use of this dataset is subject 
          to you obtaining lawful access to the [Twitter
          API](https://developer.twitter.com/en/docs
          /twitter-api), which requires you to agree to the
          [Developer Terms Policies and
          Agreements](https://developer.twitter.com/en
          /developer-terms/).}},
 publisher  = {Zenodo},
 version   = {0.0.0},
 doi     = {10.5281/zenodo.6617192},
 url     = {https://doi.org/10.5281/zenodo.6617192}
}
@inproceedings{TweetNERDNeurips2022,
 author = {Mishra, Shubhanshu and Saini, Aman and Makki, Raheleh and Mehta, Sneha and Haghighi, Aria and Mollahosseini, Ali},
 booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
 pages = {},
 title = {TweetNERD - End to End Entity Linking Benchmark for Tweets},
 volume = {2},
 year = {2022},
 eprint = {arXiv:2210.08129},
 doi = {10.48550/arXiv.2210.08129}
}

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 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).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

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

This scatter chart displays environmental score (ESG) (/ 100) against tweets in Port Hueneme. The data is about companies.

Please cite the following paper when using this dataset: N. Thakur, “Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions,” Preprints, 2022, DOI: 10.20944/preprints202206.0383.v1 Abstract The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and use cases in assisted living, military, healthcare, firefighting, and industries. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, public opinion, reviews, and feedback related to exoskeletons, for which a dataset is necessary. The Internet of Everything era of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by mining relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics including but not limited to news, current events, politics, emerging technologies, family, relationships, and career opportunities, via tweets, while sharing their views, opinions, perspectives, and feedback towards the same. Therefore, this work presents a dataset of about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. Instructions: This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data only for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. The Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets) or any similar application may be used for hydrating this dataset. Data Description This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. Filename: Exoskeleton_TweetIDs_Set1.txt (Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022) Filename: Exoskeleton_TweetIDs_Set2.txt (Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021) Filename: Exoskeleton_TweetIDs_Set3.txt (Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020) Filename: Exoskeleton_TweetIDs_Set4.txt (Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020) Filename: Exoskeleton_TweetIDs_Set5.txt (Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019) Filename: Exoskeleton_TweetIDs_Set6.txt (Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019) Filename: Exoskeleton_TweetIDs_Set7.txt (Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017) Here, the last date for May is May 21 as it was the most recent date at the time of data collection. The dataset would be updated soon to incorporate more recent tweets.

If you use the dataset, cite the paper: https://doi.org/10.1016/j.eswa.2022.117541

The most comprehensive dataset to date regarding climate change and human opinions via Twitter. It has the heftiest temporal coverage, spanning over 13 years, includes over 15 million tweets spatially distributed across the world, and provides the geolocation of most tweets. Seven dimensions of information are tied to each tweet, namely geolocation, user gender, climate change stance and sentiment, aggressiveness, deviations from historic temperature, and topic modeling, while accompanied by environmental disaster events information. These dimensions were produced by testing and evaluating a plethora of state-of-the-art machine learning algorithms and methods, both supervised and unsupervised, including BERT, RNN, LSTM, CNN, SVM, Naive Bayes, VADER, Textblob, Flair, and LDA.

The following columns are in the dataset:

➡ created_at: The timestamp of the tweet. ➡ id: The unique id of the tweet. ➡ lng: The longitude the tweet was written. ➡ lat: The latitude the tweet was written. ➡ topic: Categorization of the tweet in one of ten topics namely, seriousness of gas emissions, importance of human intervention, global stance, significance of pollution awareness events, weather extremes, impact of resource overconsumption, Donald Trump versus science, ideological positions on global warming, politics, and undefined. ➡ sentiment: A score on a continuous scale. This scale ranges from -1 to 1 with values closer to 1 being translated to positive sentiment, values closer to -1 representing a negative sentiment while values close to 0 depicting no sentiment or being neutral. ➡ stance: That is if the tweet supports the belief of man-made climate change (believer), if the tweet does not believe in man-made climate change (denier), and if the tweet neither supports nor refuses the belief of man-made climate change (neutral). ➡ gender: Whether the user that made the tweet is male, female, or undefined. ➡ temperature_avg: The temperature deviation in Celsius and relative to the January 1951-December 1980 average at the time and place the tweet was written. ➡ aggressiveness: That is if the tweet contains aggressive language or not.

Since Twitter forbids making public the text of the tweets, in order to retrieve it you need to do a process called hydrating. Tools such as Twarc or Hydrator can be used to hydrate tweets.

Market leader Facebook was the first social network to surpass one billion registered accounts and currently sits at more than three billion monthly active users. Meta Platforms owns four of the biggest social media platforms, all with more than one billion monthly active users each: Facebook (core platform), WhatsApp, Facebook Messenger, and Instagram. In the third quarter of 2023, Facebook reported around four billion monthly core Family product users. The United States and China account for the most high-profile social platforms Most top ranked social networks with more than 100 million users originated in the United States, but services like Chinese social networks WeChat, QQ or video sharing app Douyin have also garnered mainstream appeal in their respective regions due to local context and content. Douyin’s popularity has led to the platform releasing an international version of its network: a little app called TikTok. How many people use social media? The leading social networks are usually available in multiple languages and enable users to connect with friends or people across geographical, political, or economic borders. In 2025, social networking sites are estimated to reach 5.42 billion users and these figures are still expected to grow as mobile device usage and mobile social networks increasingly gain traction in previously underserved markets.

Advertising makes up 89% of its total revenue and data licensing makes up about 11%.

This scatter chart displays environmental score (ESG) (/ 100) against tweets in Jacksonville Beach. The data is about companies.

As of the first quarter of 2025, photo and video sharing app Snapchat had 460 million daily active users worldwide, up from 460 million global DAU in the fourth quarter of 2024. The app has seen steady increases in daily active users since the beginning of 2019. Snapchat is relevant for teenagers Originally launched in 2011, Snapchat has become one of the most popular social messaging and photo sharing apps worldwide; making its CEO and co-founder Evan Spiegel one of the world’s richest social media entrepreneurs. With almost 800 million active users as of April 2024, Snapchat easily ranks among the most popular social networks worldwide. According to U.S. teenagers in fall 2023, Snapchat is the second most important social network of their generation, ahead of photo sharing competitor Instagram and other networks such as Twitter or Facebook. Overall, 48 percent of U.S. internet users aged 15 to 25 years were reportedly using Snapchat, the highest usage reach among any age group. When it comes to user satisfaction with social media, Snapchat’s performance is fair to middling. According to recent survey data, the social app scored 72 out of 100 points on a consumer satisfaction scale, ranking ahead of Twitter and Facebook but behind Pinterest and eternal rival Instagram.

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.

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