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.

This is the breakdown of Twitter users by age group.

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

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

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).

Utilize our Twitter dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset provides a comprehensive understanding of social media trends, empowering organizations to refine their communication and marketing strategies. Access the entire dataset or customize a subset to fit your needs. Popular use cases include market research to identify trending topics and hashtags, AI training by reviewing factors such as tweet content, retweets, and user interactions for predictive analytics, and trend forecasting by examining correlations between specific themes and user engagement to uncover emerging social media preferences.

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

How many people use X/Twitter?

As of the first quarter of 2019, X/Twitter averaged 330 million monthly active users, a decline from its all-time high of 336 MAU in the first quarter of 2018. As of the first quarter of 2019, the company switched its user reporting metric to monetizable daily active users (mDAU).

X/Twitter

X/Twitter is a social networking and microblogging service, enabling registered users to read and post short messages called tweets. X/Twitter messages are limited to 280 characters and users are also able to upload photos or short videos. Tweets are posted to a publicly available profile or can be sent as direct messages to other users.

Part of the social platform’s appeal is the ability of users to follow any other user with a public profile, enabling users to interact with celebrities who regularly post on the social media site. Currently, the most-followed person on Twitter is singer Katy Perry with more than 107 million followers. Twitter has also become an important communications channel for governments and heads of state – U.S. President Donald Trump was the most-followed world leader on Twitter, followed by Pope Francis and Indian Prime Minister Narendra Modi.

Despite the widespread usage among the rich and famous, the decline in active users has not been impressing investors as the platform is largely reliant on delivering advertising to users in order to generate revenues. Twitter’s company revenue in 2018 amounted to three billion U.S. dollars, up from 2.44 billion in the preceding fiscal year. Twitter was only recently able to report a positive annual result for the first time, when the company generated 1.2 billion U.S. dollars in net income in 2018.

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...

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 Mexico was forecast to continuously increase between 2024 and 2028 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach ***** 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 *** 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.

Twitter Friends and hashtags

Context

This datasets is an extract of a wider database aimed at collecting Twitter user's friends (other accound one follows). The global goal is to study user's interest thru who they follow and connection to the hashtag they've used.

Content

It's a list of Twitter user's informations. In the JSON format one twitter user is stored in one object of this more that 40.000 objects list. Each object holds :

• avatar : URL to the profile picture

• followerCount : the number of followers of this user

• friendsCount : the number of people following this user.

• friendName : stores the @name (without the '@') of the user (beware this name can be changed by the user)

• id : user ID, this number can not change (you can retrieve screen name with this service : https://tweeterid.com/)

• friends : the list of IDs the user follows (data stored is IDs of users followed by this user)

• lang : the language declared by the user (in this dataset there is only "en" (english))

• lastSeen : the time stamp of the date when this user have post his last tweet.

• tags : the hashtags (whith or without #) used by the user. It's the "trending topic" the user tweeted about.

• tweetID : Id of the last tweet posted by this user.

You also have the CSV format which uses the same naming convention.

These users are selected because they tweeted on Twitter trending topics, I've selected users that have at least 100 followers and following at least 100 other account (in order to filter out spam and non-informative/empty accounts).

Acknowledgements

This data set is build by Hubert Wassner (me) using the Twitter public API. More data can be obtained on request (hubert.wassner AT gmail.com), at this time I've collected over 5 milions in different languages. Some more information can be found here (in french only) : http://wassner.blogspot.fr/2016/06/recuperer-des-profils-twitter-par.html

Past Research

No public research have been done (until now) on this dataset. I made a private application which is described here : http://wassner.blogspot.fr/2016/09/twitter-profiling.html (in French) which uses the full dataset (Millions of full profiles).

Inspiration

On can analyse a lot of stuff with this datasets :

• stats about followers & followings
• manyfold learning or unsupervised learning from friend list
• hashtag prediction from friend list

Contact

Feel free to ask any question (or help request) via Twitter : @hwassner

Enjoy! ;)

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

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles’ metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen’s d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as “school” for youth and “college” for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.

-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

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.

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

ID: A unique identifier for each tweet.

text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.

polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).

favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.

retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.

user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.

user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.

user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.

user_followers_count: The current number of followers the account has. It is a non-negative integer.

user_friends_count: The number of users that the account is following. It is a non-negative integer.

user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.

user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.

user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.

user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.

Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.

Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.

Twitter is a social news website. It can be viewed as a hybrid of email, instant messaging and sms messaging all rolled into one neat and simple package. It s a new and easy way to discover the latest news related to subjects you care about. |Attribute|Value| |-|-| |Number of Nodes: |11316811| |Number of Edges: |85331846| |Missing Values? |no| |Source:| N/A| ##Data Set Information: 1. nodes.csv — it s the file of all the users. This file works as a dictionary of all the users in this data set. It s useful for fast reference. It contains all the node ids used in the dataset 2. edges.csv — this is the friendship/followership network among the users. The friends/followers are represented using edges. Edges are directed. Here is an example. 1,2 This means user with id "1" is followering user with id "2". ##Attribute Information: Twitter is a social news website. It can be viewed as a hybrid of email, instant messaging and sms messaging all rolled into one ne