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.

How many people use social media? Social media usage is one of the most popular online activities. In 2025, over *** billion people were estimated to be using social media worldwide, a number projected to increase to over *** billion in 2030. Who uses social media? Social networking is one of the most popular digital activities worldwide, and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at ** percent. This figure is anticipated to grow as less developed digital markets catch up with other regions when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. The mobile-first market of Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe. How much time do people spend on social media? Social media is an integral part of daily internet usage. On average, internet users spend *** minutes per day on social media and messaging apps, an increase of ** minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media. What are the most popular social media platforms? Market leader Facebook was the first social network to surpass *** billion registered accounts and currently boasts approximately *** billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

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 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.

During a 2025 survey, ** percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just ** percent of Japanese respondents said the same. Large portions of social media users around the world admit that they do not trust social platforms either as media sources or as a way to get news, and yet they continue to access such networks on a daily basis. Social media: trust and consumption Despite the majority of adults surveyed in each country reporting that they used social networks to keep up to date with news and current affairs, a 2018 study showed that social media is the least trusted news source in the world. Less than ** percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than ** percent of adults in Portugal, Poland, Romania, Hungary, Bulgaria, Slovakia and Croatia said that they got their news on social media. What is clear is that we live in an era where social media is such an enormous part of daily life that consumers will still use it in spite of their doubts or reservations. Concerns about fake news and propaganda on social media have not stopped billions of users accessing their favorite networks on a daily basis. Most Millennials in the United States use social media for news every day, and younger consumers in European countries are much more likely to use social networks for national political news than their older peers. Like it or not, reading news on social is fast becoming the norm for younger generations, and this form of news consumption will likely increase further regardless of whether consumers fully trust their chosen network or not.

Facebook results as reported by [8]; Cyworld results as reported by [10].

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed. Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes: Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

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.

Survey data collected in Canada, 2019. n = 1539. Using, Age, Facebook use and meme understanding to determine differences between demographics in relation to Instagram use

Yearly citation counts for the publication titled "Social networks and opportunity recognition: A cultural comparison between Taiwan and the United States".

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

Snapshot img

Social Networking Market Size 2025-2029

The social networking market size is forecast to increase by USD 312.3 billion, at a CAGR of 21.6% between 2024 and 2029.

Major Market Trends & Insights

North America dominated the market and accounted for a 41% growth during the forecast period.
By the Type - Advertising segment was valued at USD 80.70 billion in 2023
By the Distribution Channel - Google segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 318.56 billion
Market Future Opportunities: USD 312.30 billion 
CAGR : 21.6%
North America: Largest market in 2023

Market Summary

The market continues to expand its reach and influence across various industries, with businesses recognizing its potential for customer engagement and brand awareness. According to recent studies, there are approximately 4.66 billion active social media users worldwide, representing a 13% increase from 2020. This growth is driven by the increased internet penetration and the popularity of social media platforms for personal and professional use. Social media advertisements have become a significant revenue source, with businesses investing heavily in targeted campaigns to reach their audiences.
However, privacy concerns remain a challenge, with users increasingly cautious about sharing personal information online. Despite this, the market's continuous evolution and the emergence of new trends, such as live streaming and virtual events, ensure its ongoing relevance and importance for businesses.

What will be the Size of the Social Networking Market during the forecast period?

Explore market size, adoption trends, and growth potential for social networking market Request Free Sample

The market exhibits consistent growth, with current usage accounting for approximately 3.6 billion users worldwide, representing a significant 4.5% increase year-over-year. Looking ahead, industry experts anticipate a continued expansion, with projections indicating a 5.2% annual growth rate. Notably, mobile devices account for over 90% of social media usage, underscoring the importance of optimizing platforms for this medium. Furthermore, businesses increasingly leverage social networking for marketing purposes, with advertising revenue reaching an estimated USD 84.3 billion in 2021. In comparison, the time spent on social media platforms per day has risen by 45 minutes since 2019, highlighting the growing influence of these channels on consumer behavior.
This trend is further accentuated by the integration of advanced features, such as live streaming, video content, and AI-driven recommendations, which enhance user engagement and monetization opportunities.

How is this Social Networking Industry segmented?

The social networking industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Advertising
  In-app purchase
  Paid apps


Distribution Channel

  Google
  Apple
  App Store Distribution


Service

  Communication
  Entertainment
  Socialization
  Marketing
  Customer service


Platform

  Website-based
  Mobile apps
  Hybrid platforms


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The advertising segment is estimated to witness significant growth during the forecast period.

In the dynamic and evolving landscape of digital communication, the market continues to expand, driven by innovative technologies and user engagement. According to recent data, social networking platforms accounted for approximately 30% of the total time spent online in 2021, reflecting a significant 15% increase from the previous year. Furthermore, industry experts anticipate that social media usage will continue to grow, with an estimated 25% of the global population expected to use social media by 2025. Content moderation systems play a crucial role in ensuring a safe and inclusive online environment. These systems employ advanced techniques, such as natural language processing, conversational AI, and machine learning models, to filter out inappropriate content and maintain platform governance.

User engagement metrics, including time spent on platforms, user-generated content, and social interaction dynamics, are closely monitored to optimize user experience and foster community building strategies. Platform scalability and network security protocols are essential for accommodating the increasing user base and data privacy regulations. Spam filtering techniques and link pred

How does Truth Social compare to other social media platforms? There are around 2 million active Truth Social users.

Yearly citation counts for the publication titled "User trust in social networking services: A comparison of Facebook and LinkedIn".

ABSTRACT Sharing travel experiences through social networks has become a very common practice today. Access and exposure to posted content can generate, in users, behavioral and emotional reactions capable of affecting their intention to travel. Based on this, the objective of the present work is to verify the effects of behavioral characteristics (social comparison, envy and self-presentation) on the intention to visit destinations, as displayed by users on social networks--more specifically, on Instagram. The study methodology consists of a survey applied online from May to June 2018 with Instagram users, in which we obtained 547 valid responses. For data analysis, we used descriptive statistics, factor analysis, and logistic regression to test and confirm the hypotheses of the presented theoretical model. The results indicate that envy and social comparison increase the odds of intention to visit a destination, with the strongest effect being related to the social comparison variable.

Systematic exposure to social media causes social comparisons, especially among women who compare their image to others; they are particularly vulnerable to mood decrease, self-objectification, body concerns, and lower perception of themselves. This study first investigates the possible links between life satisfaction, self-esteem, anxiety, depression, and the intensity of Instagram use with a social comparison model. In the study, 974 women age 18–49 who were Instagram users voluntarily participated, completing a questionnaire. The results suggest associations between the analyzed psychological data and social comparison types. Then, artificial neural networks models were implemented to predict the type of such comparison (positive, negative, equal) based on the aforementioned psychological traits. The models were able to properly predict between 71% and 82% of cases. As human behavior analysis has been a subject of study in various fields of science, this paper contributes towards understanding the role of artificial intelligence methods for analyzing behavioral data in psychology.

BackgroundIndividuals’ relationships are characterized by multidimensional aspects and the unique make-up of different features is more or less supportive of physical and mental health. The current study derived social network types based on an extended set of indicators reflecting the structure, function, and quality of relationships, then examined their associations with diverse physical and mental health outcomes separately for men and women.MethodsUsing samples of 620 South Korean adults (Mage = 53.52), Latent Profile Analysis (LPA) was used to uncover distinct social network types, and multiple regression analyses were conducted to examine the link between network types and health outcomes.ResultsLPA analysis derived four network types: diversified, family-(un)supported, friend- based, and restricted. The prevalence and configuration of network types differed between men and women: the family-unsupported type was more prevalent in women than in men whereas the restricted type was more prevalent in men than in women. An individual’s network type membership was significantly associated with one’s physical and mental health and the positive effects of an optimal network type and the negative effects of a non-optimal network type on mental health were much greater for women than they were for men.DiscussionThe findings suggest that women benefit more from supportive networks but that they are also more vulnerable to a lack of supportive (or the presence of conflict-filled) relationships, and highlight that having diversified and greater quality relationships, and avoiding conflicts are critical for women to have enhanced health.

Context

As part of a capstone project, we wanted to compare what social media users are talking about to what's going on in the world to see if and how social media users care about news events. We scraped data from Twitter, Reddit, reliable news sources, and Google Trending Topics.

Content

This data set includes nine tables: Twitter, news, Google Trending Topics, and six popular subreddits (news, worldnews, upliftingnews, sports, politics, television).

Twitter: trending topic, date trending, sentiment analysis scores, most common word associated with the trend, most common pairs of words associated with the trend.

News: headlines (collected from BBC News, USA Today, and the Washington Post), date the article was posted.

Google Trending Topics: trending topic, date trending.

Subreddits: post title, time, date, score (upvotes - downvotes), number of comments.

Acknowledgements

This data was collected as part of a semester project in the Capstone in Social Network Analytics at Virginia Tech, Spring 2017, taught by Siddharth Krishnan. The data was collected over a period of eight days in April 2017.

Inspiration

What do social media users care about, and in what ways do they care? What may they not know about? What types of trends appear most on each social media platform? Are people who get the majority of their news from social media able to get an accurate and comprehensive idea of what is going on? How can algorithms such as Twitter’s trending topics algorithm influence and shape what users talk about, read, and react to?

Comparison between hashtag searching results via Twitter firehose API and our datasets.

We examined how recreational runners benefit from running with others to maintain a consistent training regimen over time. We used data from the ABS project (“Always Keep Active”). Our sample consisted of more than 800 individuals who had registered to participate in the 2019 edition of the 7K or 15K Seven Hills Run (Nijmegen, The Netherlands) for the first time. Taking advantage of this three-wave, individual-level panel data, we found that increases over time in the number of co-runners (of any ability level) are related to increases in the number of weekly running sessions. The probability of turning up at the Seven Hills Run was positively related to the number of equally or less competent co-runners, and to the number with whom respondents also discussed important matters on a frequent basis. Our recreational athletes differed in the extent to which they expressed social motivations to run. However, among these athletes, the positive impact of sports partners on sport outcomes did not depend on the importance of social motives. Our study demonstrates that social networks play an important role in maintaining a consistent training habit and in reaching set goals (i.e., participating in a race).