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.

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.

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

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.

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.

A online survey conducted in the United States in May 2021 asked respondents their reasons for using social media and networking platform Twitter. Overall, 82 percent of high-volume users, those who produced 20 or more tweets per month, said that they used Twitter for entertainment reasons. Furthermore, 77 percent of high-frequency tweeters said that they used the platform as a way to express their opinions whereas 29 percent of low-frequency users said that they made use of the Twitter for this purpose. Additionally, 59 percent of high-volume users and 45 percent of low-volume users reported that Twitter has increased their understandings of current events in the last year.

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. The first 9 weeks of data (from January 1st, 2020 to March 11th, 2020) contain very low tweet counts as we filtered other data we were collecting for other research purposes, however, one can see the dramatic increase as the awareness for the virus spread. Dedicated data gathering started from March 11th to March 30th which yielded over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to February 27th, to provide extra longitudinal coverage.

The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (101,400,452 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (20,244,746 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the statistics-full_dataset.tsv and statistics-full_dataset-clean.tsv files.

More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter)

As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data. The need to be hydrated to be used.

All Cook County Tweets, starting in 2011 to 12/31/2013. As measured by Crowdbooster.

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

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! ;)

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.

According to a June 2020 survey, 42 percent of adults in the United States strongly approved of Twitter placing a content warning on one of Donald Trump's tweets. On May 29th, the social media platform placed a warning label on one of the President's tweets regarding the protesters in Minneapolis, stating that the tweet glorified violence. This was the first time that a tweet of a public figure has been labelled as such. Trump's Twitter usage has frequently incited debate and an August 2018 survey found that 61 percent of U.S. adults believed that Trump's Twitter usage was inappropriate as POTUS.

We used the Twitter API (V2) to collect all tweets, retweets, quotes and replies containing case-insensitive versions of the hashtags: #(I)StandWithPutin, #(I)StandWithRussia, #(I)SupportRussia, #(I)StandWithUkraine, #(I)StandWithZelenskyy and #(I)SupportUkraine. These were obtained from February 23rd 2022 00:00:00 UTC until March 8th 2022 23:59:59 UTC, the fortnight after Russia invaded Ukraine. We queried the hashtags with and without the `I', a total of 12 query hashtags, collecting 5,203,746 tweets. The data collected predates the beginning of the Russian invasion by one day. These hashtags were chosen as they were found to be the most trending hashtags related to the Russia/Ukraine war which could be easily identified with a particular side in the conflict. We calculated Botometer results on 483,100 (26.5%) of accounts. These accounts were randomly sampled from a list of all unique users in our dataset which posted in English. This random sample leads to an approximately uniform frequency of Tweets from accounts with Botometer labels across the time frame we considered. We include the language dependent and language independent results from Botometer, including the Complete Automation Probabilities (CAP) and each of the sub-category scores for different bot types. Moreoever, we include the display scores and raw scores from Botometer for each account. More information about the Botometer scores can be found at this link: https://rapidapi.com/OSoMe/api/botometer-pro/details You can find our paper here: https://arxiv.org/abs/2208.07038

The "Famous Keyword Twitter Replies Dataset" is a comprehensive collection of Twitter data that focuses on popular keywords and their associated replies. This dataset contains five essential columns that provide valuable insights into the Twitter conversation dynamics:

Keyword: This column represents the specific keyword or topic of interest that generated the original tweet. It helps identify the context or subject matter around which the conversation revolves.

Main_tweet: The main_tweet column contains the original tweet related to the keyword. It serves as the starting point or focal point of the conversation and often provides essential information or opinions on the given topic.

Main_likes: This column provides the number of likes received by the main_tweet. Likes serve as a measure of engagement and indicate the level of popularity or resonance of the original tweet within the Twitter community.

Reply: The reply column consists of the replies or responses to the main_tweet. These replies may include comments, opinions, additional information, or discussions related to the keyword or the original tweet itself. The replies help capture the diverse perspectives and conversations that emerge in response to the main_tweet.

Reply_likes: This column records the number of likes received by each reply. Similar to the main_likes column, the reply_likes column measures the level of engagement and popularity of individual replies. It enables the identification of particularly noteworthy or well-received replies within the dataset.

By analyzing this "Famous Keyword Twitter Replies Dataset," researchers, analysts, and data scientists can gain valuable insights into how popular keywords spark discussions on Twitter and how these discussions evolve through replies.

The dataset's information on likes allows for the evaluation of tweet and reply popularity, helping to identify influential or impactful content.

This dataset serves as a valuable resource for various applications, including sentiment analysis, trend identification, opinion mining, and understanding social media dynamics.

Number of tweets for each pairs of tweet and reply

Total has 17255 pairs of tweet/reply

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage.

The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (255,494,846 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (59,105,358 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the statistics-full_dataset.tsv and statistics-full_dataset-clean.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/

More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688)

As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. The need to be hydrated to be used.

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.

Dataset containing twitter data, namely hashed twitter id, hashed user id, tweet language, user statistics.

This dataset contains statistics related to the Unleashed Twitter account (@SAUnleashed). Unleashed is an open data competition, an initiative of the Office for Digital Government, Department of the Premier and Cabinet. The data is used to monitor the level of engagement activity with the audience, and make the communication effective in regards to the event.

This statistic presents the number of tweets by Twitter board members as of July 2019. As of the measured month, Twitter founder Jack Dorsey had posted 25,900 tweets. Second-ranked Martha Lane Fox was a similarly prolific Twitter user with over 23,000 tweets.

All Cook County Tweets, starting in 2011 to 12/31/2013. As measured by Crowdbooster.