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.

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.

In early 2025, something fascinating happened at a small community center in suburban Ohio. A town hall meeting about local road closures suddenly went viral, not because of the topic, but because a 74-year-old attendee live-tweeted the entire event using her iPad. Within hours, her posts racked up thousands of...

Social network X/Twitter is particularly popular in the United States, and as of February 2025, the microblogging service had an audience reach of 103.9 million users in the country. Japan and the India were ranked second and third with more than 70 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.

This is the breakdown of Twitter users by age group.

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

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.

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.

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

Utilize our Twitter dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset provides a comprehensive understanding of social media trends, empowering organizations to refine their communication and marketing strategies. Access the entire dataset or customize a subset to fit your needs. Popular use cases include market research to identify trending topics and hashtags, AI training by reviewing factors such as tweet content, retweets, and user interactions for predictive analytics, and trend forecasting by examining correlations between specific themes and user engagement to uncover emerging social media preferences.

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.

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

ID: A unique identifier for each tweet.

text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.

polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).

favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.

retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.

user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.

user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.

user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.

user_followers_count: The current number of followers the account has. It is a non-negative integer.

user_friends_count: The number of users that the account is following. It is a non-negative integer.

user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.

user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.

user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.

user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.

Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.

Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.

One of the biggest advantages of Twitter is the speed at which information can be passed around. People use Twitter primarily to get news and for entertainment. This is the breakdown of why people use Twitter today.

The number of Twitter users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.9 million users (+5.1 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 18.55 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 150 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).

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

ID: A unique identifier for each tweet.

text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.

polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).

favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.

retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.

user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.

user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.

user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.

user_followers_count: The current number of followers the account has. It is a non-negative integer.

user_friends_count: The number of users that the account is following. It is a non-negative integer.

user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.

user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.

user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.

user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.

Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.

Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.

The Customer Support on Twitter dataset is a large, modern corpus of tweets and replies to aid innovation in natural language understanding and conversational models, and for study of modern customer support practices and impact.

https://i.imgur.com/nTv3Iuu.png" alt="Example Analysis - Inbound Volume for the Top 20 Brands">

Context

Natural language remains the densest encoding of human experience we have, and innovation in NLP has accelerated to power understanding of that data, but the datasets driving this innovation don't match the real language in use today. The Customer Support on Twitter dataset offers a large corpus of modern English (mostly) conversations between consumers and customer support agents on Twitter, and has three important advantages over other conversational text datasets:

• Focused - Consumers contact customer support to have a specific problem solved, and the manifold of problems to be discussed is relatively small, especially compared to unconstrained conversational datasets like the reddit Corpus.
• Natural - Consumers in this dataset come from a much broader segment than those in the Ubuntu Dialogue Corpus and have much more natural and recent use of typed text than the Cornell Movie Dialogs Corpus.
• Succinct - Twitter's brevity causes more natural responses from support agents (rather than scripted), and to-the-point descriptions of problems and solutions. Also, its convenient in allowing for a relatively low message limit size for recurrent nets.

Inspiration

The size and breadth of this dataset inspires many interesting questions:

• Can we predict company responses? Given the bounded set of subjects handled by each company, the answer seems like yes!
• Do requests get stale? How quickly do the best companies respond, compared to the worst?
• Can we learn high quality dense embeddings or representations of similarity for topical clustering?
• How does tone affect the customer support conversation? Does saying sorry help?
• Can we help companies identify new problems, or ones most affecting their customers?

Content

The dataset is a CSV, where each row is a tweet. The different columns are described below. Every conversation included has at least one request from a consumer and at least one response from a company. Which user IDs are company user IDs can be calculated using the inbound field.

tweet_id

A unique, anonymized ID for the Tweet. Referenced by response_tweet_id and in_response_to_tweet_id.

author_id

A unique, anonymized user ID. @s in the dataset have been replaced with their associated anonymized user ID.

inbound

Whether the tweet is "inbound" to a company doing customer support on Twitter. This feature is useful when re-organizing data for training conversational models.

created_at

Date and time when the tweet was sent.

text

Tweet content. Sensitive information like phone numbers and email addresses are replaced with mask values like _email_.

response_tweet_id

IDs of tweets that are responses to this tweet, comma-separated.

in_response_to_tweet_id

ID of the tweet this tweet is in response to, if any.

Contributing

Know of other brands the dataset should include? Found something that needs to be fixed? Start a discussion, or email me directly at $FIRSTNAME@$LASTNAME.com!

Acknowledgements

A huge thank you to my friends who helped bootstrap the list of companies that do customer support on Twitter! There are many rocks that would have been left un-turned were it not for your suggestions!

Relevant Resources

• NLTK - casual_tokenize for social media text tokenizing, vader sentiment analysis for social media text
• SciKit Learn - BoW Count Vectorizer, Multinomial Naive Bayes Classifier
• Topic Modeling via Phrase detection with gensim
• facebook research - fastText text classifier

Our dataset comprises 1000 tweets, which were taken from Twitter using the Python programming language. The dataset was stored in a CSV file and generated using various modules. The random module was used to generate random IDs and text, while the faker module was used to generate random user names and dates. Additionally, the textblob module was used to assign a random sentiment to each tweet.

This systematic approach ensures that the dataset is well-balanced and represents different types of tweets, user behavior, and sentiment. It is essential to have a balanced dataset to ensure that the analysis and visualization of the dataset are accurate and reliable. By generating tweets with a range of sentiments, we have created a diverse dataset that can be used to analyze and visualize sentiment trends and patterns.

In addition to generating the tweets, we have also prepared a visual representation of the data sets. This visualization provides an overview of the key features of the dataset, such as the frequency distribution of the different sentiment categories, the distribution of tweets over time, and the user names associated with the tweets. This visualization will aid in the initial exploration of the dataset and enable us to identify any patterns or trends that may be present.

Dataset consists of 5 emotion labels. These labels are anger, happy, distinguish, surprise and fear. There are 800 tweets in the dataset for each label. Hence, total tweet count is 4000 for dataset.

You can use the data set in many areas such as sentiment, emotion analysis and topic modeling.

Info: Hashtags and usernames was removed in the dataset. Dataset has used many studies and researches. These researches are followed as: -(please citation this article) Güven, Z. A., Diri, B., & Cąkaloglu, T. (2020). Comparison of n-stage Latent Dirichlet Allocation versus other topic modeling methods for emotion analysis. Journal of the Faculty of Engineering and Architecture of Gazi University. https://doi.org/10.17341/gazimmfd.556104 -Güven, Z. A., Diri, B., & Çakaloğlu, T. (2019). Emotion Detection with n-stage Latent Dirichlet Allocation for Turkish Tweets. Academic Platform Journal of Engineering and Science. https://doi.org/10.21541/apjes.459447 -Guven, Z. A., Diri, B., & Cakaloglu, T. (2019). Comparison Method for Emotion Detection of Twitter Users. Proceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019. https://doi.org/10.1109/ASYU48272.2019.8946435

This dataset comprises a set of information cascades generated by Singapore Twitter users. Here a cascade is defined as a set of tweets about the same topic. This dataset was collected via the Twitter REST and streaming APIs in the following way. Starting from popular seed users (i.e., users having many followers), we crawled their follow, retweet, and user mention links. We then added those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. With this, we have a total of 184,794 Twitter user accounts. Then tweets are crawled from these users from 1 April to 31 August 2012. In all, we got 32,479,134 tweets. To identify cascades, we extracted all the URL links and hashtags from the above tweets. And these URL links and hashtags are considered as the identities of cascades. In other words, all the tweets which contain the same URL link (or the same hashtag) represent a cascade. Mathematically, a cascade is represented as a set of user-timestamp pairs. Figure 1 provides an example, i.e. cascade C = {< u1, t1 >, < u2, t2 >, < u1, t3 >, < u3, t4 >, < u4, t5 >}. For evaluation, the dataset was split into two parts: four months data for training and the last one month data for testing. Table 1summarizes the basic (count) statistics of the dataset. Each line in each file represents a cascade. The first term in each line is a hashtag or URL, the second term is a list of user-timestamp pairs. Due to privacy concerns, all user identities are anonymized.