Market leader Facebook was the first social network to surpass one billion registered accounts and currently sits at more than three billion monthly active users. Meta Platforms owns four of the biggest social media platforms, all with more than one billion monthly active users each: Facebook (core platform), WhatsApp, Messenger, and Instagram. In the third quarter of 2023, Facebook reported around four billion monthly core Family product users. The United States and China account for the most high-profile social platforms Most top-ranked social networks with more than 100 million users originated in the United States, but services like Chinese social networks WeChat, QQ, or video-sharing app Douyin have also garnered mainstream appeal in their respective regions due to local context and content. Douyin’s popularity has led to the platform releasing an international version of its network, TikTok. How many people use social media? The leading social networks are usually available in multiple languages and enable users to connect with friends or people across geographical, political, or economic borders. In 2025, social networking sites are estimated to reach 5.44 billion users, and these figures are still expected to grow as mobile device usage and mobile social networks increasingly gain traction in previously underserved markets.

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

This dataset provides detailed rankings and key metrics for 100+ social media platforms and sites in 2025. It includes information such as user base, popularity trends, and global reach. Ideal for analyzing social media growth, user engagement, and market trends. Whether you're a data scientist, marketer, or researcher, this dataset offers valuable insights into the evolving digital landscape.

Dataset

This dataset was created by Swaijit Singh

Contents

Over the five years through 2025-26, industry revenue is forecast to expand at a compound annual rate of 20.3% to reach £12.5 billion. Social media platforms are integral to people's lives, offering ways to communicate, create and view content and share information. According to Ofcom, approximately 89% of UK internet users in 2023 used social media apps or sites. Teenagers and young adults are the biggest users. Advertising is the primary revenue source for social media platforms, although subscription-based services are gaining momentum as platforms seek to diversify their incomes. TikTok is the success story of the past five years, becoming the most downloaded app between 2020 and 2022, according to Apptopia. The short-form video platform has over 30 million monthly users in the UK in 2025. After Musk's takeover, X, formerly known as Twitter, adjusted its content moderation and allowed previously banned accounts to return. As a result, over 600 advertisers pulled their ads from the site because of fears their brand may be associated with malcontent. In response to falling ad revenue, X has introduced a subscription-based service which enables users to verify themselves and boosts the number of people who view their tweets. Meta-owned Facebook and Instagram have responded by introducing a similar service. In 2025, more social media platforms are using AI to boost user engagement. This improves click-through rates and drives higher advertising revenue. Industry revenue is expected to grow by 6.3% in 2025-26. Over the five years through 2030-31, social media platforms' revenue is projected to climb at an estimated 9.2% to reach £19.4 billion. Regulations relating to how data is collected, stored, and shared will force advertisers and platforms to rethink how they can target their desired demographics. The tightening of regulations will raise industry compliance costs, weighing on profit margin. Older age groups present a new revenue opportunity for social media platforms if they can bridge the gap between passive TV consumption and interactive digital engagement. Augmented Reality (AR) technology will move beyond filters to become standard for immersive product trials, interactive ads, and virtual meetups

A collection of 500 simulated posts from fictional users. Each post entry includes post ID, user name, content length (number of characters), number of likes, and number of retweets. It’s ideal for exploring engagement patterns, building models for popularity prediction, or studying the influence of content length on interactions.

Content

The current public dataset is available in the UCI Machine Learning Repository, you can find in the following link the original publication by the authors and extra information:

https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms

Data Set Information:

This is a large dataset of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation.

The features of each instance and their definition are as follows: - IDLink (numeric): Unique identifier of news items. - Title (string): Title of the news item according to the official media sources. - Headline (string): Headline of the news item according to the official media sources. - Source (string): Original news outlet that published the news item. - Topic (string): Query topic used to obtain the items in the official media sources. - PublishDate (timestamp): Date and time of the news items' publication. - SentimentTitle (numeric): Sentiment score of the text in the news items' title. - SentimentHeadline (numeric): Sentiment score of the text in the news items' headline. - Facebook (numeric): Final value of the news items' popularity according to the social media source Facebook. - GooglePlus (numeric): Final value of the news items' popularity according to the social media source Google+. - LinkedIn (numeric): Final value of the news items' popularity according to the social media source LinkedIn .

Acknowledgements and sources

Nuno Moniz LIAAD - INESC Tec; Sciences College, University of Porto Email: nmmoniz@inesctec.pt

Luis Torgo LIAAD - INESC Tec; Sciences College, University of Porto Email: ltorgo@dcc.fc.up.pt

During a 2025 survey among marketers worldwide, around 83 percent reported using Facebook for marketing purposes. Instagram and LinkedIn followed, respectively mentioned by 78 and 69 percent of the respondents. The global social media marketing segment According to the same study, 60 percent of responding marketers intended to increase their organic use of YouTube for marketing purposes throughout that year. LinkedIn and Instagram followed with similar shares, rounding up the top three social media platforms attracting a planned growth in organic use among global marketers in 2025. Their main driver is increasing brand exposure and traffic, which led the ranking of benefits of social media marketing worldwide. Social media for B2B marketing Social media platform adoption rates among business-to-consumer (B2C) and business-to-business (B2B) marketers vary according to each subsegment's focus. While B2C professionals prioritize Facebook and Instagram, both run by Meta, Inc., due to their popularity among online audiences, B2B marketers concentrate their endeavors on Microsoft-owned LinkedIn due to its goal to connect people and companies in a corporate context.

Information

This dataset summarizes a heterogeneous set of features about articles published by Mashable in a period of two years. The goal is to predict the number of shares in social networks, i.e. how popular any given article is. The dataset is publicly available at University of California Irvine Machine Learning Repository.

Content

Mashable Inc. is a digital media website founded in 2005. It has been described as a “one stop shop” for social media. As of November 2015, it has over 6,000,000 Twitter followers and over 3,200,000 fans on Facebook.

Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field).

Acknowledgements

We thank Machine Learning Repository.

Inspiration

The goal is to predict the number of shares in social networks, i.e. how popular any given article is?

Context

Social media are today a very popular way of exchanging information with other people via the Internet. It's hard not to notice that over the years new ones are created and old ones "die". The database below presents the popularity of various social networking sites since 2009, showing the percentage of their share in the social media market.

Content

The database saved in .csv form contains several columns. The first column contains the date (YYYY-MM) of the measurement period. Each subsequent column contains the percentage of share in the social media market, given as a percentage, rounded to 2 decimal places (if the share is less than 0.5%, the value 0 remains, even though it may constitute a very small percentage of the share). We have almost 180 rows, 15 years of data for monthly periods.

Source

The database comes from the Statcounter and is made available in the operation with CC BY-SA 3.0 license which allows to copy, use and disseminate data also for commercial purposes after providing the source.

This dataset contains 600 synthetic entries simulating social media activity across three major platforms: Twitter, Reddit, and Instagram. The data was generated to analyze trends, sentiments, and user engagement patterns based on hashtags and posts. It can be useful for researchers, data analysts, and machine learning enthusiasts interested in studying social media behavior.

Dataset Structure The dataset includes the following columns:

Date: The date of the post, ranging across a simulated timeline. Platform: The social media platform where the post was made (Twitter, Reddit, or Instagram). Hashtag: The main hashtag associated with the post, such as #AI, #MachineLearning, or #Python. Post Content: The text of the post, crafted to simulate common social media interactions. Sentiment: The sentiment of the post, classified as Positive, Neutral, or Negative. Likes: The number of likes the post received. Shares: The number of shares or retweets the post received. Potential Use Cases Sentiment analysis: Train machine learning models to detect sentiment in text. Hashtag popularity analysis: Determine which hashtags are most commonly used or generate the most engagement. Engagement trends: Explore correlations between post sentiment and engagement metrics (likes/shares). Platform comparison: Compare user behavior across different social media platforms. Acknowledgments This dataset is fully synthetic and was generated using Python. It does not contain any real user data and is intended for educational and research purposes.

This dataset provides a comprehensive and diverse snapshot of social media users and their engagements across various popular platforms such as Instagram, Twitter, Facebook, YouTube, Pinterest, TikTok, and Spotify. With 100 rows of anonymized data, it offers valuable insights into the dynamic world of social media usage. 😀

Each row in the dataset represents a unique user with a designated User ID and Username to ensure anonymity. Alongside user-specific details, the dataset captures essential information, including the platform being used, the post's content, timestamp, and media type (text, image, or video). Additionally, it tracks engagement metrics such as likes, comments, shares/retweets, and user interactions, providing an overview of the user's popularity and social impact. 💬

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The dataset also includes pertinent user attributes, such as account creation date, privacy settings, number of followers, and following. The users' profiles are further enriched with demographic characteristics, including anonymized representations of their age group and gender. 🗨️

https://media.giphy.com/media/2tSodgDfwCjIMCBY8h/giphy.gif" alt="socialcat">

Hashtags, mentions, media URLs, post URLs, and self-reported location contribute to understanding user interests, content themes, and geographic distribution. Moreover, users' bios and language preferences offer insights into their passions, activities, and linguistic communication on the platforms.

This dataset provides a comprehensive analysis of the factors influencing the credibility of information on social media among Iranian users. The research focuses on identifying the most significant factors that affect the perceived credibility of information shared on various social media platforms. The dataset includes demographic information, social media usage patterns, and ratings of various attributes related to information credibility.

Key Features

• Demographic Data: Includes age, gender, education level, study field, and university.
• Social Media Usage: Details on the number of social media memberships, active platforms, and average hours spent per day.
• Credibility Factors: Ratings on various attributes such as source trustworthiness, media structure, message accuracy, and more.
• Comprehensive Coverage: Covers multiple dimensions of social media usage and information credibility.
• User-Centric Insights: Provides insights into how users perceive and interact with information on social media.

Potential Uses

• Academic Research: Investigate the factors that influence information credibility on social media.
• Social Media Analysis: Understand user behavior and preferences on social media platforms.
• Policy Development: Inform policies related to information dissemination and credibility on social media.
• Marketing and Advertising: Tailor content strategies based on user perceptions of credibility.
• User Experience Design: Improve the design and functionality of social media platforms to enhance information credibility.

Abstract:

This dataset summarizes a heterogeneous set of features about articles published by Mashable in a period of two years. The goal is to predict the number of shares in social networks (popularity).

Source:

Kelwin Fernandes (kafc ‘@’ inesctec.pt, kelwinfc ’@’ gmail.com) - INESC TEC, Porto, Portugal/Universidade do Porto, Portugal. Pedro Vinagre (pedro.vinagre.sousa ’@’ gmail.com) - ALGORITMI Research Centre, Universidade do Minho, Portugal Paulo Cortez - ALGORITMI Research Centre, Universidade do Minho, Portugal Pedro Sernadela - Universidade de Aveiro

Data Set Information:

• The articles were published by Mashable (www.mashable.com) and their content as the rights to reproduce it belongs to them. Hence, this dataset does not share the original content but some statistics associated with it. The original content be publicly accessed and retrieved using the provided urls.
• Acquisition date: January 8, 2015
• The estimated relative performance values were estimated by the authors using a Random Forest classifier and a rolling windows as assessment method. See their article for more details on how the relative performance values were set.

Attribute Information:

Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field)

Users spend an average of 19.6 hours per month on TikTok alone. This works out to be approximately 39 minutes per day.

Facial expression has been proposed as a key mechanism in forming and maintaining social relationships. Here we show that facial expressivity predicts popularity during initial social group formation. We measured the facial behaviour of strangers during naturalistic online social interactions in small groups (N = 256; in 72 groups) and calculated social network indices based on relative liking. More expressive individuals were more popular and perceived as warmer and more cooperative. In contrast, attractiveness did not leverage a social advantage. We suggest that facial expressivity is perceived as an honest indicator of potential cooperation and therefore favoured in group settings where cooperation is important to offset potential competition. Our findings show that facial expression serves an important role in determining popularity in social group settings.

YouTube gets an average of 14.3 billion total worldwide visits every month.

Introduction

Millennials on Social Media Statistics: Since the lockdown, people who never existed on the internet have created accounts on social media. As stated in these Millennials On Social Media Statistics, more than 50% of millennial users go online. The use of social media is increasing daily. Social media is gaining popularity not only among millennials but also among other generations.

However, the use of these platforms is growing extensively among all generations. These statistics guide recent insights, including the preferences for product research on social media, back-to-school shopping, etc.Â

The purpose is to identify the main factors in establishing the image of country leaders in the media by selecting, systematizing, and analyzing the key components of the process of developing the media image of politicians. The main methods in the research were system analysis, statistical method, method of scenario modeling, and comparative analysis. Such key concepts as image, character, and media portrait of political leaders at the present stage were defined; the basic features of the so-called new media, based on using Internet resources, were summarized; the main approaches and principles to the theoretical aspect of exploring the phenomenon of leaders’ image in the media space were summarized. Distinctive features, trends and characteristic factors of developing the right image (images and attitudes that are strategically effective, ethically sound, and aligned with desired outcomes and values) of presidential candidates of such countries as the U.S.A. and France were considered. The results and conclusions of this work can be used as a practical basis for future research on the above-mentioned issues, and for developing strategies and plans to promote and strengthen the image of politicians in the media space.

Snapchat now boasts over 319 million daily active users. That means it’s one of the most engaging platforms. Snapchat currently has a total user base of 800 million.