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 December 2022, X/Twitter's audience accounted for over 368 million monthly active users worldwide. This figure was projected to decrease to approximately 335 million by 2024, a decline of around five percent compared to 2022.

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

Social network X/Twitter is particularly popular in the United States, and as of April 2024, the microblogging service had an audience reach of 106.23 million users in the country. Japan and the India were ranked second and third with more than 69 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 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.

At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to the Computational Social Science research community. For example, how many active users does the platform have? What percentage of accounts on the site are bots? And, what are the dominating topics and sub-topical spheres on the platform? In a globally coordinated effort of 80 scholars to shed light on these questions, and to offer a dataset that will equip other researchers to do the same, we have collected 375 million tweets published within a 24-hour time period starting on September 21, 2022. To the best of our knowledge, this is the first complete 24-hour Twitter dataset that is available for the research community. With it, the present work aims to accomplish two goals. First, we seek to answer the aforementioned questions and provide descriptive metrics about Twitter that can serve as references for other researchers. Second, we create a baseline dataset for future research that can be used to study the potential impact of the platform's ownership change.

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.

Twitter user statistics show a varying degree of how often users login to the platform. Here’s what it looks like.

The number of Twitter users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.9 million users (+5.1 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 18.55 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).

The global number of Twitter users in was forecast to continuously increase between 2024 and 2028 by in total 74.3 million users (+17.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 503.42 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 South America and the Americas.

*** Fake News on Twitter ***

These 5 datasets are the results of an empirical study on the spreading process of newly fake news on Twitter. Particularly, we have focused on those fake news which have given rise to a truth spreading simultaneously against them. The story of each fake news is as follow:

1- FN1: A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.

2- FN2: Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."

3- FN3: Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.

4- FN4: The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.

5- FN5: In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.

The data collection has been done in two stages that each provided a new dataset: 1- attaining Dataset of Diffusion (DD) that includes information of fake news/truth tweets and retweets 2- Query of neighbors for spreaders of tweets that provides us with Dataset of Graph (DG).

DD

DD for each fake news story is an excel file, named FNx_DD where x is the number of fake news, and has the following structure:

The structure of excel files for each dataset is as follow:

• Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet:
• User ID (user who has posted the current tweet/retweet)
• The number of published tweet/retweet by the user at the time of posting the current tweet/retweet
• Language of the tweet/retweet
• Number of followers
• Number of followings (friends)
• Date and time of posting the current tweet/retweet
• Number of like (favorite) the current tweet had been acquired before crawling it
• Number of times the current tweet had been retweeted before crawling it
• Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)
• The source (OS) of device by which the current tweet/retweet was posted
• Tweet/Retweet ID
• Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)
• Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)
• Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)
• Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)
• State of the tweet which can be one of the following forms (achieved by an agreement between the annotators):

• r : The tweet/retweet is a fake news post
• a : The tweet/retweet is a truth post
• q : The tweet/retweet is a question about the fake news, however neither confirm nor deny it
• n : The tweet/retweet is not related to the fake news (even though it contains the queries related to the rumor, but does not refer to the given fake news)

DG

DG for each fake news contains two files:

• A file in graph format (.graph) which includes the information of graph such as who is linked to whom. (This file named FNx_DG.graph, where x is the number of fake news)
• A file in Jsonl format (.jsonl) which includes the real user IDs of nodes in the graph file. (This file named FNx_Labels.jsonl, where x is the number of fake news)

Because in the graph file, the label of each node is the number of its entrance in the graph. For example if node with user ID 12345637 be the first node which has been entered into the graph file then its label in the graph is 0 and its real ID (12345637) would be at the row number 1 (because the row number 0 belongs to column labels) in the jsonl file and so on other node IDs would be at the next rows of the file (each row corresponds to 1 user id). Therefore, if we want to know for example what the user id of node 200 (labeled 200 in the graph) is, then in jsonl file we should look at row number 202.

The user IDs of spreaders in DG (those who have had a post in DD) would be available in DD to get extra information about them and their tweet/retweet. The other user IDs in DG are the neighbors of these spreaders and might not exist in DD.

Dataset ini merupakan hasil dari scraping pada media sosial twitter dengan menggunakan aplikasi twint yang ditujukan pada hashtag #IndonesiaHumanRightsSOS. Scraping data dilakukan untuk cuitan yang dibuat dari tanggal 18 Desember 2020 10:59 AM s/d 19 Desember 2020 23:18 PM.

Pada dataset mengandung 106.903 Row data dengan informasi terkait: User ID, Username, Twitter Name,Tweets, dsb.

Selain itu dilampirkan juga contoh data yang telah dianalisis berupa wordcloud,username cloud, 100 most used word & most active username.

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This dataset is the result of scraping on social media twitter using the twint application aimed at the hashtag #IndonesiaHumanRightsSOS. Data scraping is done for tweets made from December 18 2020 10:59 AM to December 19 2020 23:18 PM.

The dataset contains 106,903 rows of data with related information: User ID, Username, Twitter Name, Tweets, etc.

Also there is an example of the data that has been analyzed in the form of wordcloud, username cloud, 100 most used words & most active username.

The undertaking of several studies of political phenomena in social media mandates the operationalization of the notion of political stance of users and contents involved. Relevant examples include the study of segregation and polarization online, the study of political diversity in content diets in social media, or AI explainability. While many research designs rely on operationalizations best suited for the US setting, few allow addressing more general design, in which users and content might take stances on multiple ideology and issue dimensions, going beyond traditional Liberal-Conservative or Left-Right scales. To advance the study of more general online ecosystems, we present a dataset of X/Twitter population of users in the French political Twittersphere and web domains embedded in a political space spanned by dimensions measuring attitudes towards immigration, the EU, liberal values, elites and institutions, nationalism and the environment. We provide several benchmarks validating the positions of these entities (based on both, LLM and human annotations), and discuss several applications for this dataset.

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.

Twitter_2010 data set contains tweets containing URLs that have been posted on Twitter during October 2010. In addition to tweets, we also the followee links of tweeting users, allowing us to reconstruct the follower graph of active (tweeting) users. URLs 66,059 tweets 2,859,764 users 736,930 links 36,743,448 Tweets. See also http://academictorrents.com/details/d8b3a315172c8d804528762f37fa67db14577cdb

Context

this data is collected using twitter scrapper and sampled to 2000 tweet for each user

Content

the data contain 2 columns the user and text with 13 different user with similar topics like ladygaga and rihanna .

task

tying to classify the user using user style features.

Teenagers make up the largest group of active users on TikTok.

This data is from monitoring conversations about online prostitution carried out by Twitter social media users in Indonesia over a certain period. The research results illustrate that online prostitution is dangerous for people participating in these activities. This dataset includes several data: the number of Twitter social media users who talk about online prostitution, the extent of online prostitution in Indonesia, Twitter user engagement, top tweets, top influencers, sentiment, and communication networks between Twitter users. Downloading data on Twitter regarding online prostitution conversations in Indonesia was carried out using NodeXL Software from 14/08/2023 to 21/08/2023 at 00.00.00 WIB with the keywords #availjakarta #availsurabaya #availmedan #availmakasar #availbandung #availjogja. The data collected is filtered and processed in tables, graphs, diagrams, and communication networks between Twitetr social media users. This data set is helpful for policymakers dealing with online prostitution via social media in Indonesia.

The number of Twitter users in Brazil was forecast to continuously increase between 2024 and 2028 by in total 3.4 million users (+15.79 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 24.96 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).

This dataset tracks social media engagement across platforms (Facebook, Twitter, Instagram, LinkedIn), capturing post ID, engagement type (like, comment, share), user ID, engagement count, and post date. It also includes data on 50,000 products, covering sales, stock levels, and suppliers. Ideal for analyzing social media trends, user behavior, and product performance.