At the end of the most recently reported year, microblogging and social networking company X (formerly Twitter) employed 7,500 people, up from 5,500 people in the previous year.

X/Twitter's corporate demography

In 2020, the majority of X/Twitter'semployees were male with a share of 57 percent and of a white ethnicity with 41 percent. African American and Latinx ethnicities were severely underrepresented with only 6.5 and 5.4 percent share respectively of all employees at Twitter.

Distribution of X/Twitter employees by gender and department in 2020 is revealing. In tech departments, over 73 percent of employees were male. Men also dominated the leadership departments with 62 percent. X/Twitter was founded by Jack Dorsey, Noah Glass, Biz Stone and Evan Williams in March 2006 and since then, a man has always held top positions of chairman and CEO. The only department at X/Twitter whereby women were represented well was in the Non-tech departments. In 2017, women held 53.7 percent of non-tech roles. Other social media companies The gender landscape at Facebook in 2020 followed a similar vein. The distribution of Facebook employees worldwide by gender and department revealed that men dominated the tech departments with a 76 percent share and senior level positions with a 66 percent share.

Twitter is headquartered in San Francisco, California, USA, but has dozens of worldwide offices. This includes an office in Madrid, Spain. In 2019, this office employed 18 workers, a small fraction of the company's global workforce.. This figure means a decrease by one employee compared to 2018.

In March 2022, 20.8 percent of employees in leadership roles at Twitter were of Asian ethnicity, and 8.2 percent of employees in such roles were Black. As for technical roles, 7.5 percent of employees were of Latinx ethnicity and 3.8 percent identified as multi-racial. Overall, there were a total of 7,500 Twitter employees in 2021.

In the most recently measured period, almost 45 percent of all global Twitter employees were women. Additionally, for technical roles, 30.9 percent were women, and in leadership roles, 39 percent were women. Overall, less than one percent of employees at Twitter identified as non-binary or non-conforming.

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

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.

As of March 2022, 30.8 percent of Twitter employees were of Asian ethnicity. Overall, 8.4 percent of Twitter employees were of Latinx ethnicity. Additionally, the majority of employees were white. There were a total of 7,500 Twitter employees at the end of 2021.

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

The purpose of this data collection was to test a scale for detecting verbal violence in Tweets. Workers at Mechanical Turk were first asked to complete a qualification test and then invited to code additional Tweets according to our scale. The qualification test involved a detailed explanation of each item of the scale, a walkthrough of a tweet that we had coded according to all 14 scale-items, a practice exercise, and a test. In the practice exercise, potential coders attempted to code a tweet on their own using our scale. After submitting their ratings, they were shown our own ratings for the same tweet and explanations for each of our ratings. The test component consisted of another coding task, in which coders were asked to code another tweet that we had already coded ourselves. The workers who, on test, with our ratings of that tweet on at least 11 out of the 14 items “passed” the test, earning the qualification that allowed them to participate in future coding tasks. Variables in the data include the ID of the Tweet (so that you may find it on Twitter; Twitter Terms of Service prohibit us from sharing the Tweets), the ID number we assigned to the coder, the rating that coder provided for each of the 14 items on our scale, the gender and age of the coder, and any comments the coder provided.

The cross-lingual natural disaster dataset includes public tweets collected using Twitter’s public API, filtering by location-related keywords and date, without using any additional filtering (e.g., we did not restrict the query to specific languages). We considered five disaster events between January 2020 and February 2021 that received substantial news coverage internationally.

All messages include a “language” field computed by Twitter us ing a language detection model developed specifically for tweets. We counted the number of messages per language in each event. Three of the top languages were common to all the studied events: English (ISO 639-1 code: en), Spanish (es), and French (fr). Additionally, we found several hundred messages for each event in other languages, including Catalan (ca), Tagalog (tl), Croatian (hr), German (de), Japanese (ja), Indonesian (id), and Portuguese (pt).

After collecting the data, we labelled tweets or their translation to English that contained potentially informative factual information. We name this group of tweets “informative messages.” Next, we used crowdsourcing to further categorize the messages into various informational categories. We asked three different workers to label each of the approximately 5,700 informative messages across languages. The target categories were based on an ontology from TREC-IS 2018, where we grouped some low level ontology categories into higher-level ones.

This dataset consists of 50,000 tweets pertaining to job vacancies and hiring. The tweets were collected using specific keywords such as 'Job Vacancy,' 'We are Hiring,' and 'We're Hiring'. The primary aim of this dataset is to facilitate the exploration of text pre-processing techniques and to test Natural Language Processing (NLP) skills. It also serves as a valuable resource for deriving insights into the job market from actual job postings and for analysing company and role requirements. The tweets were gathered using the snscrape Python library.

Columns

• ID: The unique identifier for each individual tweet.
• Timestamp: The precise date and time when the tweet was posted.
• User: The Twitter handle of the user responsible for posting the tweet.
• Text: The actual content of the tweet itself.
• Hashtag: Any hashtags that were included within the tweet.
• Retweets: The total count of times the tweet had been retweeted at the point of scraping.
• Likes: The total count of likes the tweet had accrued at the point of scraping.
• Replies: The total count of replies to the tweet at the point of scraping.
• Source: The application or device used to post the tweet.
• Location: The location specified on the user's Twitter profile, if available.
• Verified_Account: A Boolean value indicating whether the user's Twitter account was verified.
• Followers: The number of followers the user had at the point the tweet was scraped.
• Following: The number of accounts the user was following at the point the tweet was scraped.

Distribution

The dataset is provided in a CSV format and comprises 50,000 individual tweets or records.

Usage

This dataset is ideally suited for: * Text Pre-processing Practice: Users can experiment with various text cleaning and normalisation techniques. * Natural Language Processing (NLP) Skill Development: It serves as an excellent resource for developing and testing NLP models. * Job Market Analysis: Gaining insights into job market trends, popular roles, and hiring patterns based on social media data. * Company/Role Requirement Analysis: Examining the specific requirements or characteristics mentioned in job postings.

Coverage

The tweets in this dataset were collected between 1 January 2019 and 10 April 2023. The dataset's geographic coverage is global, as indicated by its listing. There are no specific notes on data availability for particular demographic groups or years beyond the stated collection period.

License

CC0

Who Can Use It

This dataset is intended for a variety of users, including: * Data Scientists and Analysts: For conducting text analysis, building predictive models, or extracting actionable insights. * NLP Researchers: For developing and refining algorithms related to text classification, topic modelling, or sentiment analysis on social media data. * Human Resources (HR) Professionals: To understand job market dynamics, identify hiring trends, or benchmark their own job postings. * Students and Educators: As a practical resource for learning and teaching about data analysis, social media data, and NLP.

Dataset Name Suggestions

• Job Vacancy Tweets
• Social Media Job Postings
• Hiring Tweets Dataset
• Employment Tweets
• Job Market Twitter Data

Attributes

Original Data Source: Job Vacancy Tweets

This is the breakdown of Twitter users by age group.

As of February 2025, the number of X (formerly Twitter) users in Singapore reached **** million. This represented more than a twofold increase from nearly *** million users in 2024.X (formerly Twitter) in SingaporeAs of January 2025, X (formerly Twitter) accounted for around *** percent of the social media market in Asia. In Singapore, X is the ****** most used social media platform. Singaporeans primarily use X to look out for trending topics and events that take place locally and internationally. Current developmentsIn an effort to reduce misinformation and online falsehoods, the government has been working with Facebook and X since 2019. Academics, journalists and civil society groups have taken to the platform to discuss political, social and cultural issues while some others have used it to spread hate speech. For instance, police reports were made against a user that published racially insensitive tweets regarding migrant workers.

The cross-lingual natural disaster dataset includes public tweets collected using Twitter’s public API, filtering by location-related keywords and date, without using any additional filtering (e.g., we did not restrict the query to specific languages). We considered two disaster events and two long-term natural disasters across Europe (floods and wildfires) that received substantial news coverage internationally.

Three of the top languages were common to all the studied events: English (ISO 639-1 code: en), Spanish (es), and French (fr). Additionally, we found hundreds of messages for each event in other five languages, including Arabic (ar), German (de), Japanese (ja), Indonesian (id), Italian (it) and Portuguese (pt).

After collecting the data, we labelled tweets that contained potentially informative factual information. We name this group of tweets “informative messages.” Next, we used crowdsourcing to further categorize the messages into various informational categories. We asked three different workers to label each informative messages across languages. The target categories were based on an ontology from TREC-IS 2018, where we grouped some low level ontology categories into higher-level ones.

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.

Construction and nursing are critical industries within New South Wales and Australia. Though both careers involve physically and mentally demanding work, the risks to workers during the pandemic are not well understood. In prior work, we have shown that nurses (both younger and older) were more likely to suffer the ill effects of burnout and stress than construction workers. This seems likely linked to accelerated work demands and increased pressure on nurses during the COVID-19 pandemic. Here, we subjected a large social media dataset to a series of advanced natural language processing techniques in order to explore indicators of mental status across industries before and during the COVID-19 pandemic. Objective: This social media analysis fills an important knowledge gap by comparing the social media posts of younger and older construction workers and nurses in order to obtain an insight into any potential risks to their mental health due to work health and safety issues. Methods: We analysed 1,505,638 tweets published on Twitter by younger and older (<45 vs. >45 years of age) construction workers and nurses. The study period spanned 54 months, from January 2018 to June 2022, which equates to approximately 27 months before and 27 months after the World Health Organization declared COVID-19 a global pandemic on 11 March 2020. The tweets were analysed using big data analytics and computational linguistic analyses.

3 million Russian troll tweets

This data was used in the FiveThirtyEight story Why We’re Sharing 3 Million Russian Troll Tweets.

This directory contains data on nearly 3 million tweets sent from Twitter handles connected to the Internet Research Agency, a Russian "troll factory" and a defendant in an indictment filed by the Justice Department in February 2018, as part of special counsel Robert Mueller's Russia investigation. The tweets in this database were sent between February 2012 and May 2018, with the vast majority posted from 2015 through 2017.

FiveThirtyEight obtained the data from Clemson University researchers Darren Linvill, an associate professor of communication, and Patrick Warren, an associate professor of economics, on July 25, 2018. They gathered the data using custom searches on a tool called Social Studio, owned by Salesforce and contracted for use by Clemson's Social Media Listening Center.

The basis for the Twitter handles included in this data are the November 2017 and June 2018 lists of Internet Research Agency-connected handles that Twitter provided to Congress. This data set contains every tweet sent from each of the 2,752 handles on the November 2017 list since May 10, 2015. For the 946 handles newly added on the June 2018 list, this data contains every tweet since June 19, 2015. (For certain handles, the data extends even earlier than these ranges. Some of the listed handles did not tweet during these ranges.) The researchers believe that this includes the overwhelming majority of these handles’ activity. The researchers also removed 19 handles that remained on the June 2018 list but that they deemed very unlikely to be IRA trolls.

In total, the nine CSV files include 2,973,371 tweets from 2,848 Twitter handles. Also, as always, caveat emptor -- in this case, tweet-reader beware: In addition to their own content, some of the tweets contain active links, which may lead to adult content or worse.

The Clemson researchers used this data in a working paper, Troll Factories: The Internet Research Agency and State-Sponsored Agenda Building, which is currently under review at an academic journal. The authors’ analysis in this paper was done on the data file provided here, limiting the date window to June 19, 2015, to Dec. 31, 2017.

The files have the following columns:

If you use this data and find anything interesting, please let us know. Send your projects to oliver.roeder@fivethirtyeight.com or @ollie.

The Clemson researchers wish to acknowledge the assistance of the Clemson University Social Media Listening Center and Brandon Boatwright of the University of Tennessee, Knoxville.

This data set contains the results of a survey to examine the attitudes of TurkPrime users regarding the use of Twitter monitoring to enhance clinical trial recruitment. TurkPrime was used to recruit a representative panel of users to respond to the survey.

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.

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