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 US has historically been the target country for Twitter since its launch in 2006. This is the full breakdown of Twitter users by country.

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.

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.

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

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.

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

As of February 2024, Twitter is ranked as the 12h most popular social media site in the world. The platform currently has 436 million active monthly users.

As of April 2024, 36.6 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 34.2 percent. Users aged less than 18 years accounted for 2.4 percent of users, while those aged 50 or older accounted for roughly seven 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.

*** 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.

This is the breakdown of Twitter users by age group.

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

Abstract (our paper)

Why does Smith follow Johnson on Twitter? In most cases, the reason why users follow other users is unavailable. In this work, we answer this question by proposing TagF, which analyzes the who-follows-whom network (matrix) and the who-tags-whom network (tensor) simultaneously. Concretely, our method decomposes a coupled tensor constructed from these matrix and tensor. The experimental results on million-scale Twitter networks show that TagF uncovers different, but explainable reasons why users follow other users.

Data

coupled_tensor:
The first column is the source user id (from user id), the second column is the destination user id (to user id), and the third column is the tag id.

users.id:
The first column is the user id for coupled_tensor, and the second column is the user id on Twitter.

tags.id:
The first column is the tag id for coupled_tensor, and the second column is the tag (i.e. slug or list name) on Twitter. On the tags, ###follow### and ###friend### are special tags expressing follower and following.

Publication

This dataset was created for our study. If you make use of this dataset, please cite:
Yuto Yamaguchi, Mitsuo Yoshida, Christos Faloutsos, Hiroyuki Kitagawa. Why Do You Follow Him? Multilinear Analysis on Twitter. Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). pp.137-138, 2015.
http://doi.org/10.1145/2740908.2742715

Code

Our code outputting experiment results made available at:
https://github.com/yamaguchiyuto/tagf

Note

If you would like to use larger dataset, the dataset on 1 million seed users made available at:
http://dx.doi.org/10.5281/zenodo.16267
(The dataset on 0.1 million seed users is not subset of the dataset on 1 million seed users.)

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

A December 2022 study revealed that the user base of Twitter is projected to decline in the upcoming two years. Thus, in 2023 the social network will see a decrease of nearly four percent, which in 2024 will reach down to five percent negative growth of monthly active users.

This dataset contains 653 996 tweets related to the Coronavirus topic and highlighted by hashtags such as: #COVID-19, #COVID19, #COVID, #Coronavirus, #NCoV and #Corona. The tweets' crawling period started on the 27th of February and ended on the 25th of March 2020, which is spread over four weeks.

The tweets were generated by 390 458 users from 133 different countries and were written in 61 languages. English being the most used language with almost 400k tweets, followed by Spanish with around 80k tweets.

The data is stored in as a CSV file, where each line represents a tweet. The CSV file provides information on the following fields:

Author: the user who posted the tweet

Recipient: contains the name of the user in case of a reply, otherwise it would have the same value as the previous field

Tweet: the full content of the tweet

Hashtags: the list of hashtags present in the tweet

Language: the language of the tweet

Relationship: gives information on the type of the tweet, whether it is a retweet, a reply, a tweet with a mention, etc.

Location: the country of the author of the tweet, which is unfortunately not always available

Date: the publication date of the tweet

Source: the device or platform used to send the tweet

The dataset can as well be used to construct a social graph since it includes the relations "Replies to", "Retweet", "MentionsInRetweet" and "Mentions".

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.

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.

The Observatory Knowledge Graph (OKG) is a knowledge graph with tweets on inequality in terms of the OBIO ontology (https://w3id.org/okg/obio-ontology/), which integrates social media metadata with various types of linguistic knowledge. The OKG can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse on inequality.

We retrieved tweets and retweets published from the end (30th) of May 2020 to the beginning (1st) of May 2023.

In this version of the OKG, we use a sample of 85,247 tweets, published from May 30th to August 27th, 2020. To be compliant with Twitter's policies, we remove usernames and id's, as well as the tweet texts and sentences. We also replace user IRIs with skolem IRIs through skolemization.

Access to the OKG as well as the SPARQL endpoint can be requested by sending a mail to the contact person (l.stork@uva.nl) with the following information:

1. A description of the use case
2. Affiliation of the researchers involved
3. How their work is in line with Twitter's policies: https://developer.twitter.com/en/developer-terms/policy#4-d

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! ;)