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.

Introduction

X Statistics (Twitter): X, previously referred to as Twitter, is the platform where the first tweet was posted by Jack Dorsey (the CEO of Twitter) on March 21, 2006. It took a total of 3 years, 2 months, and 1 day to achieve the significant milestone of one billion tweets on the platform.

Twitter became a publicly traded company in November 2013. Its user engagement increased a year later, with daily tweets increasing from 20,000 to 60,000 during the South by Southwest conference. Since that time, it has changed into a primary venue for users to share their daily experiences, discuss their interests, and connect with individuals globally. At that point, Twitter had approximately 200 million users.

Elon Musk acquired Twitter for $44 billion to change it into a private entity. Following this acquisition, multiple changes have occurred, including the rebranding to X. Currently, X ranks among the top six social networking applications in the United States, boasting over 500 million users worldwide.

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

During the first half of 2024, there were a total of ****** data requests for X (formerly Twittter) account information from governments with ***** of these were requested by the United States government. A further ***** were requested by the Japanese government, while ***** were requested by the European Union. Overall, ***** percent of these requests resulted in data being disclosed to the relevant authorities.

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.

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.

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.

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. The first 9 weeks of data (from January 1st, 2020 to March 11th, 2020) contain very low tweet counts as we filtered other data we were collecting for other research purposes, however, one can see the dramatic increase as the awareness for the virus spread. Dedicated data gathering started from March 11th to March 29th which yielded over 4 million tweets a day.

The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (70,569,368 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (13,535,912 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the statistics-full_dataset.tsv and statistics-full_dataset-clean.tsv files.

More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter)

As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data. The need to be hydrated to be used.

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

We used the Twitter API (V2) to collect all tweets, retweets, quotes and replies containing case-insensitive versions of the hashtags: #(I)StandWithPutin, #(I)StandWithRussia, #(I)SupportRussia, #(I)StandWithUkraine, #(I)StandWithZelenskyy and #(I)SupportUkraine. These were obtained from February 23rd 2022 00:00:00 UTC until March 8th 2022 23:59:59 UTC, the fortnight after Russia invaded Ukraine. We queried the hashtags with and without the `I', a total of 12 query hashtags, collecting 5,203,746 tweets. The data collected predates the beginning of the Russian invasion by one day. These hashtags were chosen as they were found to be the most trending hashtags related to the Russia/Ukraine war which could be easily identified with a particular side in the conflict. We calculated Botometer results on 483,100 (26.5%) of accounts. These accounts were randomly sampled from a list of all unique users in our dataset which posted in English. This random sample leads to an approximately uniform frequency of Tweets from accounts with Botometer labels across the time frame we considered. We include the language dependent and language independent results from Botometer, including the Complete Automation Probabilities (CAP) and each of the sub-category scores for different bot types. Moreoever, we include the display scores and raw scores from Botometer for each account. More information about the Botometer scores can be found at this link: https://rapidapi.com/OSoMe/api/botometer-pro/details You can find our paper here: https://arxiv.org/abs/2208.07038

This dataset contains statistics on the usage patterns of the official City of Seattle Twitter accounts, as well their outreach impact. Jun 2016 - Jan 2017

Descriptive statistics for Twitter metrics.

This dataset contains statistics related to the Unleashed Twitter account (@SAUnleashed). Unleashed is an open data competition, an initiative of the Office for Digital Government, Department of the Premier and Cabinet. The data is used to monitor the level of engagement activity with the audience, and make the communication effective in regards to the event.

In early 2025, something fascinating happened at a small community center in suburban Ohio. A town hall meeting about local road closures suddenly went viral, not because of the topic, but because a 74-year-old attendee live-tweeted the entire event using her iPad. Within hours, her posts racked up thousands of...

The file contains Tweet IDs* for COVID-19 related tweets collected in August, 2022 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/).Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overviewNote: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).

Basic statistics of the three tweet collections.

More and more, social scientists are using (big) digital behavioral data for their research. In this context, the social network and microblogging platform Twitter is one of the most widely used data sources. In particular, geospatial analyses of Twitter data are proving to be fruitful for examining regional differences in user behavior and attitudes. However, ready-to-use spatial information in the form of GPS coordinates is only available for a tiny fraction of Twitter data, limiting research potential and making it difficult to link with data from other sources (e.g., official statistics and survey data) for regional analyses. We address this problem by using the free text locations provided by Twitter users in their profiles to determine the corresponding real-world locations. Since users can enter any text as a profile location, automated identification of geographic locations based on this information is highly complicated. With our method, we are able to assign over a quarter of the more than 866 million German tweets collected to real locations in Germany. This represents a vast improvement over the 0.18% of tweets in our corpus to which Twitter assigns geographic coordinates. Based on the geocoding results, we are not only able to determine a corresponding place for users with valid profile locations, but also the administrative level to which the place belongs. Enriching Twitter data with this information ensures that they can be directly linked to external data sources at different levels of aggregation. We show possible use cases for the fine-grained spatial data generated by our method and how it can be used to answer previously inaccessible research questions in the social sciences. We also provide a companion R package, nutscoder, to facilitate reuse of the geocoding method in this paper.

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. The first 9 weeks of data (from January 1st, 2020 to March 11th, 2020) contain very low tweet counts as we filtered other data we were collecting for other research purposes, however, one can see the dramatic increase as the awareness for the virus spread. Dedicated data gathering started from March 11th to March 22nd which yielded over 4 million tweets a day.

The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (40,823,816 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (7,479,940 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the statistics-full_dataset.tsv and statistics-full_dataset-clean.tsv files.

More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter)

As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data. The need to be hydrated to be used.

Replication data for the paper “The speed of news in Twitter (X) versus radio”. If you use these datasets in a published work, please cite our paper!The dataset contains the following files:Tweet IDs:auto-elite-tweet-ids.csv.gz: Tweet IDs for automatically detected event analysis, elite usersauto-firehose-tweet-ids.csv.gz: Tweet IDs for automatically detected event analysis, firehosemanual-elite-tweet-ids.csv.gz: Tweet IDs for manually detected even analysis, elite usersmanual-firehose-tweet-ids.csv.gz: Tweet IDs for manually detected even analysis, firehoseManually detected events data:manual-radio-raw.csv.gz: Item-level information for radio speaker turns used in manually detected event analysismanual-radio-ticks.csv: Radio “tick” data for manually detected event analysis (i.e., counts of keyword mentions by 15-minute bin)Automatically detected events item-level data:auto-radio-sample.csv.gz: Item-level information for radio speaker turns used in automatically detected event analysisAutomatically detected events story-level data:auto-radio-story-stats.csv: Statistics about automatically detected eventsauto-radio-story-selected.csv: Selected events after filtering out non-news eventsauto-radio-story-stats-con.csv: Statistics about automatically detected events as restricted to conservativesauto-radio-story-stats-lib.csv: Statistics about automatically detected events as restricted to liberalsauto-radio-story-cdfs.npy.gz: Empirical CDFs for automatically detected eventsauto-radio-story-cdfs-con.npy.gz: Empirical CDFs for automatically detected events as restricted to conservativesauto-radio-story-cdfs-lib.npy.gz: Empirical CDFs for automatically detected events as restricted to liberals