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.

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.

In 2020, 189 countries were represented through an official presence on Twitter, either by personal or institutional accounts run by heads of state and government and foreign ministers. During the measured period, U.S. President Donald Trump was ranked first, having accumulated over 81.1 million Twitter followers on his personal account. The official @POTUS account was ranked fifth with 30.2 million followers worldwide. Heads of state on Twitter Twitter is a very conversational social platform, allowing users to communicate in a very public manner. Foreign ministries utilize Twitter to expand their online presence and digital diplomatic networks, and government officials are encouraged to interact with the public. The most conversational world leader on Twitter is the Government of Nepal, with 96 percent of their tweets being @ replies to other Twitter users. Another more subtle layer of Twitter diplomacy is the mutual following of peers between official heads of state, minister and other government accounts – as of June 2020, the Foreign Ministry of Iceland (@MFAIceland) was ranked first, having 147 mutual connections with other world leaders and foreign ministries on Twitter. During the measured period, @realDonaldTrump, @POTUS and the @WhiteHouse Twitter accounts did not follow any other foreign leaders. In 2018, the account of the U.S. State Department had only 59 mutual peer connections on Twitter, painting a relatively isolated picture in terms of international political communications. Trump on Twitter Donald Trump’s prolific Twitter usage is a hotly debated topic. The President uses Twitter on a daily basis to make comments about other politicians, celebrities and daily news, sometimes antagonizing others with his controversial statements. According to an August 2018 survey, 61 percent of U.S. adults stated that Trump's use of Twitter as President of the United States was inappropriate, while only 24 percent of respondents said the opposite. In total, 90 percent of respondents who identified as Democrats thought that Trump's Twitter use was inappropriate; while on the other end of the political spectrum only 35 percent of respondents identifying as Republicans reported having the same opinion.

The number of X (Twitter) followers of the Major League Baseball team Colorado Rockies decreased from April 2024 to November 2024. In the last recorded month, the team's social media account had around 0.61 million followers.

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

A Twitter follower-followee graph with 269,640 nodes and 6,818,501 edges from [Kwak], and we obtain the ground truth labels from [SybilSCAR]. Among them 178377 are benign and 91263 are Sybil. We divide 9000 Sybil and 17000 benign users (about 10%) from them as the training set and test on the overall social graph.

H. Kwak, C. Lee, H. Park, and S. Moon, “What is twitter, a social network or a news media?” in WWW, 2010 B. Wang, L. Zhang, and N. Z. Gong, “SybilSCAR: Sybil detection in online social networks via local rule based propagation,” in IEEE INFOCOM, 2017.

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.)

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.

Between April 2023 and April 2024, the number of Facebook fans of the National Basketball Association (NBA) franchise Dallas Mavericks remained at **** million. On the other hand, the team's official X (formerly Twitter) page recorded a marginal increase in followers, rising from **** million to **** million.

The number of X (Twitter) followers of the Major League Baseball team Oakland Athletics decreased from May 2023 to November 2024. In the last recorded month, the team's social media account had around 0.6 million followers.

Approximately *** million users worldwide followed the X (formerly Twitter) account of the Ukrainian President Volodymyr Zelenskyy (@ZelenskyyUa) as of January 28, 2025. The follower count increased tenfold between January and February 2022 after Russia invaded Ukraine on February 24, 2022. Zelenskyy, who won the presidential election in April 2019, opened an X account in the same month. Zelenskyy publishes posts in Ukrainian and English.

Content

This dataset is a subset of all the Twitter-users, along with their connections and locations.

The graph created by considering the users as nodes and their connections as edges is a connected component of the total Twitter graph (i.e. for every user in the subgraph, all their connections in the original graph are contained within the subgraph).

Although Twitter is a directed graph (follower-following relation is not mutual. "X follows Y" does not imply "Y follows X"), we have considered the directed edges as undirected. Hence, if u→v is present in the original graph but v→u is not, we have added the edge v→u for every u and v.

There are two directories: one with 10 million users and the other with 1 million. Each directory contains two .txt files: location, and user.

The location file contains the latitude and longitude of each user. The file format is:

lat_1, long_1
lat_2, long_2
lat_3, long_3
...

where lat_1 and long_1 are the latitude and longitude of user number 1 respectively, and so on.

The user file contains the adjacency list of each user. The k-th row of this file enumerates the friends of user number k.

Bibliographic References

If you use this dataset, please cite it as:

@article{DBLP:journals/pvldb/GhoshACHSL18,
author  = {Bishwamittra Ghosh and
Mohammed Eunus Ali and
Farhana Murtaza Choudhury and
Sajid Hasan Apon and
Timos Sellis and
Jianxin Li},
title   = {The Flexible Socio Spatial Group Queries},
journal  = {{PVLDB}},
volume  = {12},
number  = {2},
pages   = {99--111},
year   = {2018},
url    = {http://www.vldb.org/pvldb/vol12/p99-ghosh.pdf},
timestamp = {Mon, 03 Dec 2018 16:45:54 +0100},
biburl  = {https://dblp.org/rec/bib/journals/pvldb/GhoshACHSL18},
}

Source Publications

@inproceedings{DBLP:conf/kdd/LiWDWC12,
author  = {Rui Li and Shengjie Wang and Hongbo Deng and Rui Wang and Kevin Chen-Chuan Chang},
title   = {Towards social user profiling: unified and discriminative influence model for inferring home locations},
booktitle = {KDD},
year   = {2012},
pages   = {1023-1031}
}

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.

*** 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 description sentence in the profile of the user who has published the tweet/retweet

The number of published tweet/retweet by the user at the time of posting the current tweet/retweet

Date and time of creation of the account by which the current tweet/retweet has been posted

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.

Description These files contain the data employed in the experiments described in Bruno Ordozgoiti and Aristides Gionis. 2019. Reconciliation k-median: Clustering with Non-Polarized Representatives. In Proceedings of the 2019 World Wide Web Conference (WWW’19), May 13–17, 2019, San Francisco, CA, USA. Twitter ID's have been anonymized. # Contents domain_mentions.txt: Each line contains a domain name, a user ID and the number of times this user has mentioned this domain name in a tweet. format: domain_name

The number of X (Twitter) followers of the Major League Baseball team Arizona Diamondbacks experienced an overall increase from September 2011 to November 2024. In the last recorded month, the team's social media account had around 0.65 million followers.

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.

Typical runtimes and space requirements for systems performing local community detection on the Twitter Follower network of 700 million vertices and 20 billion edges and producing 100 vertex output communities.

This is the breakdown of Twitter users by age group.