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.

This project is inspired on https://github.com/neo4j-graph-examples/twitter-v2.

Twitter Graph

Show data from your personal Twitter account

The Graph Your Network application inserts your Twitter activity into Neo4j.

https://neo4jsandbox.com/guides/twitter/img/twitter-data-model.svg" alt="">

Content

~10 MB of graphs data (CSV)

43.325 node labels - Hashtag - Link - Me - Source - Tweet - User

57.896 relationship types - AMPLIFIES - CONTAINS - FOLLOWS - INTERACTS_WITH - MENTIONS - POSTS - REPLY_TO - RETWEETS - RT_MENTIONS - SIMILAR_TO - TAGS - USING

476 million Twitter tweets

Dataset information

467 million Twitter posts from 20 million users covering a 7 month period
from June 1 2009 to December 31 2009. We estimate this is about 20-30% of
all public tweets published on Twitter during the particular time frame.

For each public tweet the following information was available:

Author                                 
Time                                  
Content                                

We have no Twitter social graph (who-follows-whom graph) available. You can find a copy of the graph at http://an.kaist.ac.kr/traces/WWW2010.html
(thanks to Haewoon Kwak, et al.).

Dataset statistics
Number of users 17,069,982
Number of tweets 476,553,560
Number of URLs 181,611,080
Number of Hashtags 49,293,684
Number of re-tweets 71,835,017

Source (citation)
J. Yang, J. Leskovec. Temporal Variation in Online Media. ACM Intl.
Conf. on Web Search and Data Mining (WSDM '11), 2011.

As per request from Twitter the data is no longer available.

http://an.kaist.ac.kr/traces/WWW2010.html :

What is Twitter, a Social Network or a News Media?

Haewoon Kwak (http://an.kaist.ac.kr/~haewoon),
Changhyun Lee (http://an.kaist.ac.kr/~chlee),
Hosung Park (http://an.kaist.ac.kr/~hosung),
and Sue Moon (http://an.kaist.ac.kr/~sbmoon)

Proceedings of the 19th International World Wide Web (WWW) Conference,
April 26-30, 2010, Raleigh NC (USA)

Twitter, a microblogging service less than three years old, commands more
than 41 million users as of July 2009 and is growing fast. Twitter users
tweet about any topic within the 140-character limit and follow others to
receive their tweets. The goal of this paper is to study the topological
characteristics of Twitter and its power as a new medium of information
sharing.

We have crawled the entire Twitter site and obtained 41.7 million user
profiles, 1.47 billion social relations, 4,262 trending topics, and 106
million tweets. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effective diameter, and low
reciprocity, which all mark a deviation from known characteristics of human social networks~\cite{Newman03}. In order to identify influentials on
Twitter, we have ranked users by the number of followers and by PageRank
and found two rankings to be similar. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the
number of followers and that from the popularity of one's tweets. We have
analyzed the tweets of top trending topics and reported on their temporal
behavior and user participation. We have classified the trending topics
based on the active period and th...

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.

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

The number of X (Twitter) followers of the Major League Baseball team New York Mets increased considerably from September 2011 to November 2024. In the last recorded month, the team's social media account had around 1.27 million followers.

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.

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.

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 Table (in csv format) link_status_search_with_ordering_real_csv contains tweets with the following information link: URL within the text of the tweet id: tweet id create_at: date added to the db create_at_long inreplyto_screen_name: screen name of user this tweet is replying to inreplyto_user_id: user id of user this tweet is replying to source: device from which the tweet originated bad_user_id: alternate user id user_screen_name: tweeting user screen name order_of_users: tweet s index within sequence of tweets of the same URL user_id: user id Table (in csv format) distinct_users_from_search_table_real_map contains names of tweeting users, and the following information for

With around 4.65 million followers, the New England Patriots was the most followed National Football League team account on X (Twitter) as of November 2024. In second place was the Dallas Cowboys, with around 4.42 million followers on their social media account.

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}
}

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.

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

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.

This statistic provides information on the most popular luxury brands on Twitter, ranked by number of followers. In 2020, the French luxury brand Chanel was ranked first with 13 million Twitter followers, followed by Burberry, Dior, and Louis Vuitton with 8 million followers each.

This is the breakdown of Twitter users by age group.

This dataset is structured as a graph, where nodes represent users and edges capture their interactions, including tweets, retweets, replies, and mentions. Each node provides detailed user attributes, such as unique ID, follower and following counts, and verification status, offering insights into each user's identity, role, and influence in the mental health discourse. The edges illustrate user interactions, highlighting engagement patterns and types of content that drive responses, such as tweet impressions. This interconnected structure enables sentiment analysis and public reaction studies, allowing researchers to explore engagement trends and identify the mental health topics that resonate most with users.

The dataset consists of three files: 1. Edges Data: Contains graph data essential for social network analysis, including fields for UserID (Source), UserID (Destination), Post/Tweet ID, and Date of Relationship. This file enables analysis of user connections without including tweet content, maintaining compliance with Twitter/X’s data-sharing policies. 2. Nodes Data: Offers user-specific details relevant to network analysis, including UserID, Account Creation Date, Follower and Following counts, Verified Status, and Date Joined Twitter. This file allows researchers to examine user behavior (e.g., identifying influential users or spam-like accounts) without direct reference to tweet content. 3. Twitter/X Content Data: This file contains only the raw tweet text as a single-column dataset, without associated user identifiers or metadata. By isolating the text, we ensure alignment with anonymization standards observed in similar published datasets, safeguarding user privacy in compliance with Twitter/X's data guidelines. This content is crucial for addressing the research focus on mental health discourse in social media. (References to prior Data in Brief publications involving Twitter/X data informed the dataset's structure.)

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

*** 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.

When Lisa Johnson opened her bakery in Chicago in early 2024, she didn’t expect that one viral tweet about her pistachio cruffins would transform her side hustle into a full-fledged business. But that’s exactly what happened, just a single post on X (formerly Twitter) brought in thousands of new followers,...