As of October 2025, social network X (formerly known as Twitter) was most popular in the United States, with an audience reach of approximately 99.04 million users. Japan ranked second, recording more than 71 million users on the platform. 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.

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

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

As of October 2025, micro-blogging platform X (formerly Twitter) was more popular with men than women, with male audiences accounting for 64.4 percent of global users. Additionally, users between the ages of 25 and 34 were particularly active on X/Twitter, making up more than 37 percent of users worldwide. How many people use? Although X/Twitter holds its status as a mainstream social media site, it falls short in comparison to other well-known platforms in terms of user numbers. As of early 2022, X/Twitter had around 436 million monthly active users, whilst Meta’s Facebook reached almost three billion MAU. Overall, the United States is home to over 105 million X/Twitter users, making up Twitter’s largest audience base, followed by Japan, India, and the United Kingdom, respectively. How is Twitter used? X/Twitter is utilized by its audience for many different purposes. In May 2021, over 80 percent of high-volume X/Twitter users (defined as users who tweet around 20 times per month) in the United States reported using the platform for entertainment, whilst 78 percent said they used it as a way to stay informed. High-volume X/Twitter users were far more likely to use the service as a means of expressing their opinion. Furthermore, in 2022, over half of social media users in the U.S. used Twitter as a news resource.  

Tweets and User Engagement

Twitter Data: Tweet Characteristics and Engagement Metrics

By Krystal Jensen [source]

About this dataset

The dataset Twitter Data: Tweets and User Interactions provides comprehensive information about tweets and user interactions on the popular social media platform Twitter. The dataset includes various attributes that shed light on the characteristics and engagement metrics of tweets, allowing for in-depth analysis of user behavior and content performance.

One of the key variables in this dataset is the Klout score, which represents the influence and reputation of the Twitter users who posted the tweets. This numeric metric helps assess the impact a user has on their audience and provides insights into their social media presence.

Another essential attribute is the text content of each tweet. By examining this textual data, analysts can uncover valuable information about trending topics, opinions, sentiments, conversations, or news shared by users. It serves as a primary source for understanding what people share publicly on Twitter.

The dataset Twitter+data+in+sheets.csv serves as a reliable resource for conducting research or performing analytics that require detailed information about Twitter activity. It covers aspects such as tweet characteristics (including length and language), engagement metrics (such as retweets and favorites), sentiment analysis (revealing positive or negative emotions expressed), as well as individual user details.

By utilizing this extensive dataset, researchers can gain valuable insights into patterns of online communication within Twitter's vast network. They can identify influential individuals with high Klout scores who have substantial reach among their followers or communities. Additionally, they can analyze various aspects related to tweet content such as sentiment analysis to understand public opinion trends or measure engagement levels through counts like retweets and favorites.

Overall, this dataset serves as an invaluable resource for anyone interested in comprehensively analyzing tweets' characteristics, exploring how users interact with them across different dimensions like popularity or sentiment analysis groups—or examining correlations between Klout scores with other factors influencing engagement levels like time posted

How to use the dataset

Welcome to the Twitter Data: Tweets and User Interactions dataset! This dataset provides valuable insights into tweet characteristics and user engagement on Twitter. Here is a useful guide on how to make the most out of this dataset:

• Understanding the Columns: There are two main columns in this dataset:

  • Klout Score (Numeric): The Klout score indicates the influence of the user who posted the tweet. A higher Klout score suggests greater influence and reach.
  • Text Content of Tweet (Text): This column contains the actual text content of each tweet.

• Analyzing Tweet Characteristics: The text content column will help you understand various aspects of tweets, such as language, sentiment, trending topics, or specific keywords used by users. You can perform text analysis techniques like word frequency analysis or sentiment analysis to gain insights into tweet characteristics.

• Examining User Engagement: The Klout score provides a measure of user influence on Twitter. By analyzing this column, you can identify highly influential users who generate higher engagement rates with their tweets. You can further explore interactions (likes, retweets, replies) between these influential users and other Twitter users mentioned in their tweets.

• Identifying Trends and Patterns: With this dataset's rich information about tweet content and user engagement, you can identify popular trends or patterns among highly engaged tweets or influential users over different time periods.

Remember that dates are not included in this guide since they were not provided in the original request for creating it.

Please note that it is essential to responsibly use this data for any analysis or research purposes while adhering to ethical considerations related to privacy rights and data usage policies set by both Kaggle platform rules as well as any relevant privacy regulations.

Best regards, [Your Name]

Research Ideas

• Analyzing the relationship between Klout score and the content of tweets: This dataset can be used to investigate whether there is a correlation between a user's Klout score (a measure of their social media influence) and the characteristics of their tweets. By examining factors such as tweet length, sentiment, and engagement metrics, researchers can gain...

According to a global online survey conducted in 2022, 55 percent of weekly X/Twitter users stated that they wanted to see informative content on the social media site. Overall, almost 40 percent of users said that they wanted to see relevant psots, whilst a third of users said they wanted to see funny content on the social media app. Although the majority of users desired informative content, 35 percent said they liked to see trendy posts.

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

-This Dataset was gathered by crawling Twitter's REST API using the Python library tweepy 3. This dataset contains the tweets of the 20 most popular twitter users (with the most followers) whereby retweets are neglected. These accounts belong to public people, such as Katy Perry and Barack Obama, platforms, YouTube, Instagram, and television channels shows, e.g., CNN Breaking News and The Ellen Show. -Consequently, the dataset contains a mix of relatively structured tweets, tweets written in a formal and informative manner, and completely unstructured tweets written in a colloquial style. Unfortunately, the geocoordinates were not available for those tweets. - H -This Dataset has been used to generate reserach paper under title "Machine Learning Techniques for Anomalies Detection in Post Arrays". -Crawled attributes are: Author (Twitter User), Content (Tweet), Date_Time, id (Twitter User ID), language (Tweet Langugage), Number_of_Likes, Number_of_Shares. Overall: 52543 tweets of top 20 users in twitter Screen_Name #Tweets Time span (in days) TheEllenShow 3,147 - 662 jimmyfallon 3,123 - 1231 ArianaGrande 3,104 - 613 YouTube 3,077 - 411 KimKardashian 2,939 - 603 katyperry 2,924 - 1,598 selenagomez 2,913 - 2,266 rihanna 2,877 - 1,557 BarackObama 2,863 - 849 britneyspears 2,776 - 1,548 instagram 2,577 - 456 shakira 2,530 - 1,850 Cristiano 2,507 - 2,407 jtimberlake 2,478 - 2,491 ladygaga 2,329 - 894 Twitter 2,290 - 2,593 ddlovato 2,217 - 741 taylorswift13 2,029 - 2,091 justinbieber 2,000 - 664 cnnbrk 1,842 - 183 (2017)

Explore key Twitter user statistics, including growth trends, engagement metrics, demographics, tweet volume, and platform activity!

At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to the Computational Social Science research community. For example, how many active users does the platform have? What percentage of accounts on the site are bots? And, what are the dominating topics and sub-topical spheres on the platform? In a globally coordinated effort of 80 scholars to shed light on these questions, and to offer a dataset that will equip other researchers to do the same, we have collected 375 million tweets published within a 24-hour time period starting on September 21, 2022. To the best of our knowledge, this is the first complete 24-hour Twitter dataset that is available for the research community. With it, the present work aims to accomplish two goals. First, we seek to answer the aforementioned questions and provide descriptive metrics about Twitter that can serve as references for other researchers. Second, we create a baseline dataset for future research that can be used to study the potential impact of the platform's ownership change.

As of February 2025, 37.5 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 32.1 percent. Users aged less than 18 years accounted for two percent of users, while those aged 50 or older accounted for roughly 7.3 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.

The dataset consists of various columns containing information related to tweets posted on Twitter. Each row in the dataset represents a single tweet. Here's an explanation of the columns in the dataset from a third-person perspective:

Tweet: This column contains the actual text content of the tweet. It includes the message that the user posted on Twitter. Tweets can vary in length from a few characters to the maximum allowed by Twitter.

Sentiment: This column indicates the sentiment or emotional tone of the tweet. Sentiment can be classified into categories such as positive, negative, or neutral. It reflects the overall opinion or attitude expressed in the tweet.

Username: This column contains the username of the Twitter account that posted the tweet. Each Twitter user has a unique username that identifies their account.

Timestamp: This column contains the timestamp indicating when the tweet was posted. It includes information about the date and time when the tweet was published on Twitter.

Retweets: This column represents the number of times the tweet has been retweeted by other Twitter users. A retweet is when a user shares another user's tweet with their followers.

Likes: This column indicates the number of likes or favorites received by the tweet. Users can express their appreciation for a tweet by liking it.

Hashtags: This column contains any hashtags included in the tweet. Hashtags are keywords or phrases preceded by the "#" symbol, used to categorize or label tweets and make them more discoverable.

Mentions: This column includes any Twitter usernames mentioned in the tweet. Mentions are when a user tags another user in their tweet by including their username preceded by the "@" symbol.

Location: This column provides information about the location associated with the tweet. It may include details such as the city, state, country, or geographical coordinates from which the tweet was posted, if available.

Source: This column specifies the source or platform used to post the tweet. It indicates whether the tweet was posted from the Twitter website, a mobile app, or a third-party application.

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.

MrBeast's Twitter Audience Engagement Analysis, 2017-2020

Likes, Retweets, Quotes, and More

By Twitter [source]

About this dataset

This dataset provides an invaluable opportunity to gain key insights into user engagement, content consumption and interaction with MrBeast's tweets since 2017. The data offers invaluable analysis of audience engagement, such as likes, retweets and quotes of MrBeast's Tweets. Utilizing this dataset can help you understand how users interact with different types of content, what factors drive audience engagement, and how audience behavior changes over time. This is an essential resource for digital marketing research, surveys, analytics and social media projects related to Twitter campaigns. Whether you're a researcher or marketer looking to understand the impact of MrBeast’s tweets on its followers or seeking to develop strategies for promoting products and services—this dataset is your ticket!

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How to use the dataset

1)Start by examining the columns in this dataset. There are 8 columns that each provide unique insights and can be used to help understand the user interaction with MrBeast's tweets and analyze how these interactions have changed over time. Each column will be discussed in detail below.

2)The first column is ‘ID’, which provides a numerical identification of each individual tweet. For researchers interested in analyzing data on an individual basis, this is an important feature to consider when evaluating overall trends and patterns on a particular tweet or across the entirety of MrBeast's tweets.

3)Following ID is 'Tweet', which contains the text content associated with a particular tweet. This column can then be used to identify topics of discussion or popular hashtags associated with certain posts that may not have been noticed when reviewing data from automated metrics or analytics platforms like Twitter Analytics or Hootsuite Insights respectively.

4)Continuing on we can find ‘Date’, which provides information about when each specific post was made available for public viewing onto Twitter’s platform (in YYYY-MM-DD format). Analyzing post history from start to finish – as well as different times groups within this time period – helps us gain insight into how users interact differently depending on current events taking place at either the local or global level at that given moment in time.
5)The next three columns—'Likes', 'Retweets', and 'Quotes—provide insights into respective levels of engagement for any given post relative to other posts published by MrBeast throughout its timeline thus far.' Likes shows us how many users found a tweet interesting/enjoyable while Retweets indicates how many users shared it across their own personal streams; meanwhile Quotes records how many individuals took it upon themselves to copy particular sections/statements they felt were meaningful enough warrant preserving publicly elsewhere within their own feeds.' These represent designated actions taken towards content published by Mr Beast which has undoubtedly influenced audience interaction over recent years (e.g., #BlackLivesMatter trending during summer 2020 leading into awareness campaigns for US Presidential Election held later that year). And lastly…

6)'URL' supplies direct links towards where these posts origin from — i.e., https://twitter dot com slash mrbeast dot etcetera ― making it especially convenient if you're ever looking look up further background information behind why certain posts were made during seminal moments such what we just spoke about above (

Research Ideas

• Brand monitoring - This dataset can be used to track brand performance over time by analyzing MrBeast Tweets and how their audience is engaging with them. This might include tracking things like the average number of likes and retweets per tweet, or which topics are the most popular among users. By understanding how people are interacting with the brand on Twitter, brands could potentially make more informed decisions around their communications strategies and content marketing campaigns.

• Audience segmentation - This dataset could be used to determine customer segments based on their engagement with MrBeast's tweets, such as those who predominantly retweet or quote his posts versus those who only like them. By understanding which types of customers engage more or less often with certain types of content, companies may be able to target specific groups for their outreach strategies or product promotions in order to increase engagement levels amongst this group of customers. ...

We collected the data of a Twitter user using Tweepy to access the Twitter API. We crawled the list of each user account’s followers. Twitter allowed a request of a maximum of 200 tweets per time window and because of limitations of the Twitter API, we could only make a request every 15 minutes. Next, we obtained the most recent tweets of each user in the study. We extracted the most common hashtags used in the sample tweets and crawled the most recent 50 tweets that contained each hashtag and tweets that mentioned a particular user, for example ’@username.’ Initially, we chose 101 user accounts and documented the attributes of each user’s account (number of followers, a list of followers, and the recent tweets of each follower).

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".

Although counts of tweets citing academic papers are used as an informal indicator of interest, little is known about who tweets academic papers and who uses Twitter to find scholarly information. Without knowing this, it is difficult to draw useful conclusions from a publication being frequently tweeted. This study surveyed 1,912 users that have tweeted journal articles to ask about their scholarly-related Twitter uses. Almost half of the respondents (45%) did not work in academia, despite the sample probably being biased towards academics. Twitter was used most by people with a social science or humanities background. People tend to leverage social ties on Twitter to find information rather than searching for relevant tweets. Twitter is used in academia to acquire and share real-time information and to develop connections with others. Motivations for using Twitter vary by discipline, occupation, and employment sector, but not much by gender. These factors also influence the sharing of different types of academic information. This study provides evidence that Twitter plays a significant role in the discovery of scholarly information and cross-disciplinary knowledge spreading. Most importantly, the large numbers of non-academic users support the claims of those using tweet counts as evidence for the non-academic impacts of scholarly research.

Dataset ini merupakan hasil dari scraping pada media sosial twitter dengan menggunakan aplikasi twint yang ditujukan pada hashtag #IndonesiaHumanRightsSOS. Scraping data dilakukan untuk cuitan yang dibuat dari tanggal 18 Desember 2020 10:59 AM s/d 19 Desember 2020 23:18 PM.

Pada dataset mengandung 106.903 Row data dengan informasi terkait: User ID, Username, Twitter Name,Tweets, dsb.

Selain itu dilampirkan juga contoh data yang telah dianalisis berupa wordcloud,username cloud, 100 most used word & most active username.

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This dataset is the result of scraping on social media twitter using the twint application aimed at the hashtag #IndonesiaHumanRightsSOS. Data scraping is done for tweets made from December 18 2020 10:59 AM to December 19 2020 23:18 PM.

The dataset contains 106,903 rows of data with related information: User ID, Username, Twitter Name, Tweets, etc.

Also there is an example of the data that has been analyzed in the form of wordcloud, username cloud, 100 most used words & most active username.

According to a February 2023 survey, 27 percent of X (formerly Twitter) users in the United Kingdom had used the service in the past week. In addition, 13 percent of UK users said they had used X for news in the past week. Overall, users in Turkey were the the most likely to have used X recently for news, with 26 percent reporting to have done so. Users in Slovakia, Romania, Hungary, Austria, and Germany were not using X for news as much as other European countries were.