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 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.

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.

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 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.

This is the breakdown of Twitter users by age group.

As of February 2025, micro-blogging platform X (formerly Twitter) was more popular with men than women, with male audiences accounting for 63.7 percent of global users. Additionally, users between the ages of 25 and 34 were particularly active on X/Twitter, making up more than 37 percent of users worldwide. How many people use? Although X/Twitter holds its status as a mainstream social media site, it falls short in comparison to other well-known platforms in terms of user numbers. As of early 2022, X/Twitter had around 436 million monthly active users, whilst Meta’s Facebook reached almost three billion MAU. Overall, the United States is home to over 105 million X/Twitter users, making up Twitter’s largest audience base, followed by Japan, India, and the United Kingdom, respectively. How is Twitter used? X/Twitter is utilized by its audience for many different purposes. In May 2021, over 80 percent of high-volume X/Twitter users (defined as users who tweet around 20 times per month) in the United States reported using the platform for entertainment, whilst 78 percent said they used it as a way to stay informed. High-volume X/Twitter users were far more likely to use the service as a means of expressing their opinion. Furthermore, in 2022, over half of social media users in the U.S. used Twitter as a news resource.  

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

We collected the data of a Twitter user using Tweepy to access the Twitter API. We crawled the list of each user account’s followers. Twitter allowed a request of a maximum of 200 tweets per time window and because of limitations of the Twitter API, we could only make a request every 15 minutes. Next, we obtained the most recent tweets of each user in the study. We extracted the most common hashtags used in the sample tweets and crawled the most recent 50 tweets that contained each hashtag and tweets that mentioned a particular user, for example ’@username.’ Initially, we chose 101 user accounts and documented the attributes of each user’s account (number of followers, a list of followers, and the recent tweets of each follower).

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

The number of Twitter users in France was forecast to continuously increase between 2024 and 2028 by in total 0.8 million users (+8.18 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 10.59 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 Luxembourg and Netherlands.

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

Context

The following data-set consists of very simple twitter analytics data, including text, user information, confidence, profile dates etc.

Content

Basically the dataset is self explanatory and the objective is basically to classify which gender is more likely to commit typos on their tweets.

Inspiration

Since this dataset contains pretty simple and easy-to-deal-with features, I hope many emerging NLP enthusiasts who have been developing just basic linear/naive models until now, can explore how to apply these techniques to real word tweet data.

The US has the largest number of Twitter users with over a 100 million users. They account for about 16.7% of all Twitter users worldwide.

The number of Twitter users in Mexico was forecast to continuously increase between 2024 and 2028 by in total 3.1 million users (+23.7 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 16.21 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 United States.

-This Dataset was gathered by crawling Twitter's REST API using the Python library tweepy 3. This dataset contains the tweets of the 20 most popular twitter users (with the most followers) whereby retweets are neglected. These accounts belong to public people, such as Katy Perry and Barack Obama, platforms, YouTube, Instagram, and television channels shows, e.g., CNN Breaking News and The Ellen Show. -Consequently, the dataset contains a mix of relatively structured tweets, tweets written in a formal and informative manner, and completely unstructured tweets written in a colloquial style. Unfortunately, the geocoordinates were not available for those tweets. - H -This Dataset has been used to generate reserach paper under title "Machine Learning Techniques for Anomalies Detection in Post Arrays". -Crawled attributes are: Author (Twitter User), Content (Tweet), Date_Time, id (Twitter User ID), language (Tweet Langugage), Number_of_Likes, Number_of_Shares. Overall: 52543 tweets of top 20 users in twitter Screen_Name #Tweets Time span (in days) TheEllenShow 3,147 - 662 jimmyfallon 3,123 - 1231 ArianaGrande 3,104 - 613 YouTube 3,077 - 411 KimKardashian 2,939 - 603 katyperry 2,924 - 1,598 selenagomez 2,913 - 2,266 rihanna 2,877 - 1,557 BarackObama 2,863 - 849 britneyspears 2,776 - 1,548 instagram 2,577 - 456 shakira 2,530 - 1,850 Cristiano 2,507 - 2,407 jtimberlake 2,478 - 2,491 ladygaga 2,329 - 894 Twitter 2,290 - 2,593 ddlovato 2,217 - 741 taylorswift13 2,029 - 2,091 justinbieber 2,000 - 664 cnnbrk 1,842 - 183

This dataset comprises a set of information cascades generated by Singapore Twitter users. Here a cascade is defined as a set of tweets about the same topic. This dataset was collected via the Twitter REST and streaming APIs in the following way. Starting from popular seed users (i.e., users having many followers), we crawled their follow, retweet, and user mention links. We then added those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. With this, we have a total of 184,794 Twitter user accounts. Then tweets are crawled from these users from 1 April to 31 August 2012. In all, we got 32,479,134 tweets. To identify cascades, we extracted all the URL links and hashtags from the above tweets. And these URL links and hashtags are considered as the identities of cascades. In other words, all the tweets which contain the same URL link (or the same hashtag) represent a cascade. Mathematically, a cascade is represented as a set of user-timestamp pairs. Figure 1 provides an example, i.e. cascade C = {< u1, t1 >, < u2, t2 >, < u1, t3 >, < u3, t4 >, < u4, t5 >}. For evaluation, the dataset was split into two parts: four months data for training and the last one month data for testing. Table 1summarizes the basic (count) statistics of the dataset. Each line in each file represents a cascade. The first term in each line is a hashtag or URL, the second term is a list of user-timestamp pairs. Due to privacy concerns, all user identities are anonymized.

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

This dataset is a collection of scraped public twitter updates used in coordination with an academic project to study the geolocation data related to twittering. From the explanatory PDF in the dataset collection: We provide both training set and test set (collected from September 2009 to January 2010) in the paper You Are Where You Tweet: A Content-Based Approach to Geo-locating Twitter Users in CIKM 2010. The training set contains 115,886 Twitter users and 3,844,612 updates from the users. All the locations of the users are self-labeled in United States in city-level granularity. The test set contains 5,136 Twitter users and 5,156,047 tweets from the users. All the locations of users are uploaded from their smart phones with the form of "UT: Latitude,Longitude". Please cite the following paper when using the dataset. Z. Cheng, J. Caverlee, and K. Lee. You Are Where You Tweet: A Content-Based Approach to Geo-locating Twitter Users. In Proceeding of the 19th ACM Conference on Information and Knowledge Management (CIKM), Toronto, Oct 2010. (Bibtex)