As of April 2024, 36.6 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 34.2 percent. Users aged less than 18 years accounted for 2.4 percent of users, while those aged 50 or older accounted for roughly seven 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 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.

This is the breakdown of Twitter users by age group.

As of January 2024, micro-blogging platform X (formerly Twitter) was more popular with men than women, with male audiences accounting for 60.9 percent of global users. Additionally, users between the ages of 25 and 34 were particularly active on X/Twitter, making up more than 38 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.  

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 January 2024, 22.4 percent of X (formerly Twitter) users were men aged between 25 and 34 years. Overall, almost 20 percent of users were men aged between 18 and 24 years. X has a high share of male users when compared to other popular social media platforms.

As of September 2024, Millennials, those born between 1980 and 1994, made up 36 percent of X's (formerly Twitter) user base in the United Kingdom. Overall, 31 percent of users in the UK belonged to Generation X, and 24 percent were of Generation Z.

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.

We looked at 10

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).

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

Replication materials documentation for "Twitter data reveal six distinct environmental personas" This replication code and dataset accompanies the manuscript linked at: https://doi.org/10.1002/fee.2510. I provide a description of the replication datasets below and include a SHA256 checksum that you can use to ensure the integrity of the downloaded file (please execute shasum -a 256 FILENAME in the command line to verify, or use some other utility to find the SHA256 checksum for each file). ### Datasets * MeanViewpoints.tsv contains the mean and standard error of the mean (SEM) for the issue viewpoints shown in Figure 1 for the six personas in a "long data" format * Columns: * variable: name of the environmental issue * mean: persona-level mean viewpoint value for that issue * SEM: standard error of the mean * Persona: abbreviated name for the six personas (SMA: Smart alecks, GEN: Generalists, STE: Stewards, CLC: Climate concerned, TEC: Technocrats, RES: Reserved) * SHA256: 5d540edcb39c8d7a14db315b5eaeed83689021ec43cbe16a1c7eb4467c943098 * UserTweetIDs.txt contains one tweet ID per user of the 1+ million users in our sample. These tweet IDs can be "hydrated" and used to find the users sampled in our study. * TweetID: single column listing one tweet ID per user * SHA256: fbe1da240a5ab9d9aebac0aabbde247e6eeebfa77c5471cdb6136f45110b1111 * EnvironmentalPundits.tsv contains the user names and IDs for the environmental pundits whose timelines were used as the data source to train the probabilistic latent Dirichlet allocation topic model. * Columns: * Screenname: User name (e.g. GretaThunberg, which you can use to navigate to twitter.com/GretaThunberg) * ID: User ID * SHA256: a6a987d934dea75e8ba2329820d6cfe354af0991f2bdbd4746b0f83ad6dafaa3 * Persona_PoliticalIdeology.tsv provides the mean political ideology score for the six personas * Columns: * mean: mean political ideology score * SEM: standard error of the mean * Persona: abbreviated name for the six personas * SHA256: e568d9737cbd7c0b1b1ce61a6c9c8294f14a62d934446cc0d618ebf091bf1a13 * US_geography.tsv shows the state-level ranks for each persona * Columns: * name: State name * Persona: abbreviated name for the six personas * Rank: Rank for the 50 states (+ Washington DC) * SHA256: 8ba7e0ca437639656e25a473c4aec281e828e59941af847d8865bf4eddf1371d ### Code * Scraper.py provides code that can be used to obtain user information from the UserTweetIDs.txt data file above to reproduce the user set in our analysis. * Plotting.R provides code to reproduce the plots in the main text. Effective digital environmental communication is integral to galvanizing public support for conservation in the age of social media. Environmental advocates require messaging strategies suited to social media platforms, including ways to identify, target, and mobilize distinct audiences. Here, we provide – to the best of our knowledge – the first systematic characterization of environmental personas on social media. Beginning with 1 million environmental nongovernmental organization (NGO) followers on Twitter, of which 500,000 users met data quality criteria, we identified six personas that differ in their expression of 21 environmental issues. General consistency in the proportional composition of personas was detected across 14 countries with sufficiently large samples. Within the US, although the six personas varied in their mean political ideology, we did not observe that the personas split along political party lines. Our results pave the way for environmental advocates – including NGOs, public agencies, and researchers – to use audience segmentation methods like the one discussed here to target and tailor messages to distinct constituencies at speed and scale. This repository contains several tabular files that can be used to query user data from Twitter or reproduce the main results in the main text of the article. These data may only be used for publically accessible research and may not be used for private, for-profit use. This replication code and dataset accompanies the manuscript linked at: https://doi.org/10.1002/fee.2510. Please see the main text of the article and Supplementary Information for more details on how the data were gathered and processed.

A hand-labeled training (50,000 tweets labeled twice) and evaluation set (10,000 tweets labeled twice) for hate speech on Slovenian Twitter. The data files contain tweet IDs, hate speech type, hate speech target, and annotator ID. For obtaining the full text of the dataset, please contact the first author.

Hate speech type:

1. Appropriate - has no target

2. Inappropriate (contains terms that are obscene, vulgar; but the text is not directed at any person specifically) - has no target

3. Offensive (including offensive generalization, contempt, dehumanization, indirect offensive remarks)

4. Violent (author threatens, indulges, desires, or calls for physical violence against a target; it also includes calling for, denying, or glorifying war crimes and crimes against humanity)

Hate speech target:

1. Racism (intolerance based on nationality, ethnicity, language, towards foreigners; and based on race, skin color)

2. Migrants (intolerance of refugees or migrants, offensive generalization, call for their exclusion, restriction of rights, non-acceptance, denial of assistance…)

3. Islamophobia (intolerance towards Muslims)

4. Antisemitism (intolerance of Jews; also includes conspiracy theories, Holocaust denial or glorification, offensive stereotypes…)

5. Religion (other than above)

6. Homophobia (intolerance based on sexual orientation and / or identity, calls for restrictions on the rights of LGBTQ persons

7. Sexism (offensive gender-based generalization, misogynistic insults, unjustified gender discrimination)

8. Ideology (intolerance based on political affiliation, political belief, ideology… e.g. “communists”, “leftists”, “home defenders”, “socialists”, “activists for…”)

9. Media (journalists and media, also includes allegations of unprofessional reporting, false news, bias)

10. Politics (intolerance towards individual politicians, authorities, system, political parties)

11. Individual (intolerance toward any other individual due to individual characteristics; like commentator, neighbor, acquaintance )

12. Other (intolerance towards members of other groups due to belonging to this group; write in the blank column on the right which group it is)

Training dataset

The training set is sampled from data collected between December 2017 and February 2020. The sampling was intentionally biased to contain as much hate speech as possible. A simple model was used to flag potential hate speech content and additionally, filtering by users and by tweet length (number of characters) was applied. 50,000 tweets were selected for annotation.

Evaluation dataset

The evaluation set is sampled from data collected between February 2020 and August 2020. Contrary to the training set, the evaluation set is an unbiased random sample. Since the evaluation set is from a later period compared to the training set, the possibility of data linkage is minimized. Furthermore, the estimates of model performance made on the evaluation set are realistic, or even pessimistic, since the evaluation set is characterized by a new topic: Covid-19. 10,000 tweets were selected for the evaluation set.

Annotation results

Each tweet was annotated twice: In 90% of the cases by two different annotators and in 10% of the cases by the same annotator. Special attention was devoted to evening out the overlap between annotators to get agreement estimates on equally sized sets.

Ten annotators were engaged for our annotation campaign. They were given annotation guidelines, a training session, and a test on a small set to evaluate their understanding of the task and their commitment before starting the annotation procedure. Annotator agreement in terms of Krippendorff Alpha is around 0.6. Annotation agreement scores are detailed in the accompanying report files for each dataset separately.

The annotation process lasted four months, and it required about 1,200 person-hours for the ten annotators to complete the task.

How high is the brand awareness of X (Twitter) in the UK?When it comes to social media users, brand awareness of X (Twitter) is at 84 % in the UK. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is X (Twitter) in the UK?In total, 30 % of UK social media users say they like X (Twitter). What is the usage share of X (Twitter) in the UK?All in all, 34 % of social media users in the UK use X (Twitter). How loyal are the users of X (Twitter)?Around 30 % of social media users in the UK say they are likely to use X (Twitter) again. What's the buzz around X (Twitter) in the UK?In February 2024, about 21 % of UK social media users had heard about X (Twitter) in the media, on social media, or in advertising over the past four weeks. If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

The size and share of this market is categorized based on Ad Formats (Image Ads, Video Ads, Carousel Ads, Stories Ads, Collection Ads) and Platforms (Facebook, Instagram, Twitter, LinkedIn, Snapchat) and Target Audience (Demographic Targeting, Behavioral Targeting, Interest Targeting, Geo-Targeting, Custom Audiences) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

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

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

Social Advertising & Social Media Market Outlook

The global social advertising and social media market size was valued at approximately USD 120 billion in 2023 and is projected to reach USD 350 billion by 2032, growing at a CAGR of 12.5% from 2024 to 2032. The primary growth drivers include increasing internet penetration, the proliferation of smartphones, and the rising adoption of social media platforms for marketing purposes.

One of the key growth factors of the social advertising and social media market is the exponential increase in internet users and the widespread penetration of smartphones. This digital transformation has provided businesses with unprecedented access to a wide audience base, allowing them to target potential customers more effectively through social media platforms. The convenience and reach of social media advertising are unparalleled, enabling businesses to engage with a global audience in real-time and deliver personalized content, which significantly boosts brand visibility and customer engagement.

Moreover, advancements in data analytics and artificial intelligence have further fueled the growth of this market. These technologies enable businesses to analyze consumer behavior and preferences more accurately, allowing for more targeted and personalized advertising campaigns. The ability to measure and track the effectiveness of advertising efforts in real-time provides companies with valuable insights, enabling them to optimize their marketing strategies and achieve higher conversion rates. This data-driven approach has become a cornerstone of modern advertising, contributing to the robust growth of the social advertising and social media market.

The rising popularity of social media influencers and content creators has also played a significant role in market growth. Influencer marketing has emerged as a powerful tool for brands to reach their target audience authentically and organically. Collaborations with influencers enable brands to leverage their followers and create a sense of trust and credibility. This form of advertising is particularly effective in reaching younger demographics who are more likely to engage with content from influencers than traditional advertisements.

From a regional perspective, North America currently holds the largest market share in the social advertising and social media market, driven by the high adoption rate of social media platforms and advanced digital marketing infrastructure. Europe and Asia Pacific are also witnessing significant growth, with the latter expected to demonstrate the highest CAGR during the forecast period. The rapid digitalization and increasing internet penetration in emerging economies within the Asia Pacific region, such as India and China, are major contributors to this growth.

Platform Analysis

The social advertising and social media market can be segmented by platform into Facebook, Instagram, Twitter, LinkedIn, Snapchat, TikTok, and others. Facebook remains the dominant platform, due to its extensive user base and advanced advertising tools. Businesses of all sizes utilize Facebook's robust advertising capabilities to target specific demographics with personalized content. Despite privacy concerns and regulatory scrutiny, Facebook's vast data collection and analytical capabilities make it an attractive platform for advertisers seeking to maximize their reach and engagement.

Instagram, owned by Facebook, has also seen substantial growth, particularly among younger users. The platform's focus on visual content makes it ideal for brands looking to engage with consumers through images and short videos. Instagram's Story feature and IGTV have opened new avenues for advertising, allowing brands to create more immersive and interactive content. The influence of social media influencers on Instagram is particularly noteworthy, as it provides brands with an authentic way to reach potential customers.

Twitter, known for its real-time interaction and trending topics, offers unique opportunities for brands to engage with consumers through promoted tweets and trending hashtags. The platform's concise format and emphasis on immediacy make it suitable for time-sensitive promotions and announcements. However, the challenge lies in the brief lifespan of tweets, requiring advertisers to frequently update their content to remain visible and relevant.

LinkedIn, the go-to platform for professional networking, is increasingly being leveraged for B2B advertising. The platform's