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.

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

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

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.

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.)

*** 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.

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.

The number of X (Twitter) followers of the Major League Baseball team San Diego Padres increased substantially from September 2011 to November 2024. In the last recorded month, the team's social media account had around 0.7 million followers.

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.

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.

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

kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval

This repo contains the TwitterFaveGraph dataset from our paper kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval. [PDF] [HuggingFace Datasets] This work is licensed under a Creative Commons Attribution 4.0 International License.

  TwitterFollowGraph

TwitterFollowGraph is a bipartite directed graph of users (consumer) nodes to author (producer) nodes… See the full description on the dataset page: https://huggingface.co/datasets/Twitter/TwitterFollowGraph.

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.

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.)

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".

This is the breakdown of Twitter users by age group.

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 user_id mention_count

domains_ideology_score.txt: Domain names and their ideology score, estimated as described in (Lahoti et al. WSDM 2018). Note: missing scores can be retrieved from supplementary data in https://doi.org/10.1093/poq/nfw006 format: domain_name ideology_score

follow_graph.txt: The Twitter follower graph. Each line contains a user id and the user id of one of its followers. format: user_id follower_user_id

representatives.txt: US Congress representatives, each with Twitter handle and polarity score computed using Barbera's method (Barbera, 2015). format: rep_name website_url district twitter_handle party barbera_polarity_score

user_polarity.txt: User ID's and polarity score computed using Barbera's method (Barbera, 2015). format: user_id barbera_polarity_score

The number of Facebook fans and Twitter followers of the National Football League franchise Jacksonville Jaguars experienced an overall increase from 2012 to 2024. As of November 2024, the franchise had around 0.68 million fans on their Facebook page and 0.88 million followers on their official Twitter page.

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.

Online Social Networks (OSN) are used by millions of users, daily. This user-base shares and discovers different opinions on popular topics.
Social influence of large groups may be influenced by user believes or be attracted the interest in particular news or products. A large number of users, gathered in a single group or number of followers, increases the probability to influence OSN users.
Botnets, collections of automated accounts controlled by a single agent, are a common mechanism for exerting
maximum influence. Botnets may be used to better infiltrate the social graph over time and create an illusion of community
behaviour, amplifying their message and increasing persuasion.

This paper investigates Twitter botnets, their behavior, their interaction with user communities and their evolution over time.
We analyze a dense crawl of a subset of Twitter traffic, amounting to nearly all interactions by Greek-speaking Twitter users for a period
of 36 months.

The collected users are labeled as botnets, based on long term and frequent content similarity events. We detect over a million events, where seemingly unrelated accounts tweeted nearly identical content, at almost the same time. We filter these concurrent content injection events and detect a set of 1,850 accounts that repeatedly exhibit this pattern of behavior, suggesting that they are fully or in part controlled and orchestrated by the same entity. We find botnets that appear for brief intervals and disappear, as well as botnets that evolve and grow, spanning the duration of our dataset. We analyze statistical differences between the bot accounts and human users, as well as the botnet interactions with the user communities and the Twitter trending topics.