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.

This is the breakdown of Twitter users by age group.

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

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.

A table mapping common Twitter/X goals to the key metrics to track and what those metrics indicate about performance.

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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.

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...

Statistics about new users added each day since the start of Twitter (X), from the 2.8 billion Twitter profile dataset.

The "New Users" field shows the number of new users with a "created_at" field (some suspended accounts did not include this field).

The "New Public" field shows the number of public user accounts added that day (with the "protected" flag set to 0).

The "Friends Per User", "Followers Per User", and "Statuses Per User" are the average number of friends/followers/statuses that new users that day had as of November, 2022. So a user that created an account in 2009, posted once in 2010, and 99 times in 2021 will show 100.

The "Suspended" field is the number of accounts created that day that were known to be suspended as of November, 2022. For this dataset, a user is considered suspended if the screen_name, statuses_count, and created_at fields are blank in the original 2.8 billion Twitter profile dataset.

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

Explore key Twitter statistics, including user growth, tweet volume, engagement rates, advertising trends, and global usage. Stay informed!

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).

Use our Twitter Posts dataset to analyze detailed information about tweets, including post content, author username, hashtags, mentions, likes, retweets, replies, and posting date. Popular use cases include sentiment analysis, tracking public opinion, and evaluating the performance of social media campaigns. Over 31M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Post ID Username & Name Description/Content Posting Date Photos & Videos Quoted Posts Tagged Users Replies Count Reposts Count Likes Count Views Count External URLs Hashtags Followers Count Biography Profile Image Verification Status Bookmarks And much more

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 number of Twitter users in France was forecast to continuously increase between 2024 and 2028 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach ***** million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Luxembourg and Netherlands.

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

Real-time X (Twitter) post volume, engagement metrics, and sentiment analysis providing insights into social conversations, brand perception, and influencer impact for institutional investment research.

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! ;)

Context

The following data-set consists of very simple twitter analytics data, including text, user information, confidence, profile dates etc.

Content

Basically the dataset is self explanatory and the objective is basically to classify which gender is more likely to commit typos on their tweets.

Inspiration

Since this dataset contains pretty simple and easy-to-deal-with features, I hope many emerging NLP enthusiasts who have been developing just basic linear/naive models until now, can explore how to apply these techniques to real word tweet data.