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.

As of February 2025, 24.5 percent of X (formerly Twitter) users were men aged between 25 and 34 years. Overall, almost 19 percent of users were men aged between 18 and 24 years. X has a high share of male users when compared to other popular social media platforms.

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

In 2024, accounts on X (formerly Twitter) posted an average of 3.55 times per week, down from 5.73 times per week in 2023. In October 2022, Elon Musk acquired Twitter for 44 billion US dollars.

This is the breakdown of Twitter users by age group.

In 2024, posts on X (formerly Twitter) generated an average of 31.46 likes, down from 37.82 likes in 2023. Mentions and reposts also declines year-over-year, and in 2024, the average post on the platform received 1.56 likes and 8.47 mentions.

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.

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

In 2024, X (formerly Twitter) posts generated an average of 2,121 impressions, up from 1,206 impressions in 2023. In 2022, Elon Musk's purchase of Twitter sent shockwaves through the tech world, and much has changed on the platform since.

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

In 2024, X (formerly Twitter) posts had an average of 3.4 replies, up from an average of 1.64 replies in 2023. Elon Musk's X account is the profile with the most followers on the platform.

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.

During the first half of 2024 there were 34,497 pieces of content removed from X due to personal privacy violations, which include the publishing or sharing of other people's private information. These types of violations are also known as doxxing. Overall, 30,450 of these pieces of content were reported manually by users of the platform.

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

According to a survey conducted in March 2023, 40 percent of X (formerly Twitter) users reported it was extremely or very likely that they would still be using the platform in a year's time. Overall, 35 percent said it was somewhat likely, and one-quarter of respondents stated it was not very likely, or not likely at all.

With over 611 million monthly active users, building a huge Twitter following is not an easy task. These are the top 25 accounts with the most followers on Twitter right now.

The dataset contains two data sheets, summarizing the results of a class activity involving the use of Twitter to share information and to learn. Table S1 is a summary of the tweets produced by user GeoHumana_UV (teacher/subject account) between 2020-09-16 and 2020-12-15, plus interactions recorded for each tweet (including number of impressions, URL clicks, likes, retweets and replies). All data provided by Twitter Analytics. Table S2 is a summary of student activity related to the class activity for the same period of time. It includes anonymized data on tweeting activity, level of interaction, performance and self-assessment by each participating student. Data provided by Twitter Analytics and the participants Data on tweets, tweet counts and tweet interaction counts were downloaded from https://twitter.com and https://analytics.twitter.com/user/GeoHumana_UV/home. Data on student performance and self-assessment were provided by participants during the development of the subject 34956 Human Geography in the year 2020-2021 at the University of Valencia. The raw data was manipulated for anonymization (removal of student ID) and presentation (column order, cell borders, etc.) purposes. Students' self-assessment reports were machine-translated from Spanish (using DeepL) and Catalan (using Google Translate). Datasheets are provided as Open Document Format for Office Applications (ODF), extended from xml, with .ods extension. The most common office packages, such as Libreoffice, can open the format

One of the biggest advantages of Twitter is the speed at which information can be passed around. People use Twitter primarily to get news and for entertainment. This is the breakdown of why people use Twitter today.

This is a multimodal dataset used in the paper "On the Role of Images for Analyzing Claims in Social Media", accepted at CLEOPATRA-2021 (2nd International Workshop on Cross-lingual Event-centric Open Analytics), co-located with The Web Conference 2021. The four datasets are curated for two different tasks that broadly come under fake news detection. Originally, the datasets were released as part of challenges or papers for text-based NLP tasks and are further extended here with corresponding images. 1. clef_en and clef_ar are English and Arabic Twitter datasets for claim check-worthiness detection released in CLEF CheckThat! 2020 Barrón-Cedeno et al. [1]. 2. lesa is an English Twitter dataset for claim detection released by Gupta et al.[2] 3. mediaeval is an English Twitter dataset for conspiracy detection released in MediaEval 2020 Workshop by Pogorelov et al.[3] The dataset details like data curation and annotation process can be found in the cited papers. Datasets released here with corresponding images are relatively smaller than the original text-based tweets. The data statistics are as follows: 1. clef_en: 281 2. clef_ar: 2571 3. lesa: 1395 4. mediaeval: 1724 Each folder has two sub-folders and a json file data.json that consists of crawled tweets. Two sub-folders are: 1. images: This Contains crawled images with the same name as tweet-id in data.json. 2. splits: This contains 5-fold splits used for training and evaluation in our paper. Each file in this folder is a csv with two columns

According to a survey conducted in March 2023, 69 percent of female X/Twitter users had taken a break from the platform for several weeks or more in the past 12 months, compared to 54 percent of male users. Overall 46 percent of male users reported they had not taken a break from X/Twitter within the past year.