How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Introduction

Average Time Spent On Social Media: In 2024, social media became an ever stronger engine for everyday life. People all over the world are spending time scrolling, posting, watching, and chatting on TikTok, YouTube, Facebook, and many other platforms. Knowing the average time spent on these social media platforms at all would inform better digital habits, better business decisions, and healthier relationships with technology.

Between October 2020 and March 2021, users aged between 40 and 44 years were the demographic spending the largest amount of time engaging with social media apps, with an average of 59.85 minutes per day. Global users aged 19 or younger spent the least amount of time on social media apps, while users aged 50 years or older spent approximately 56.43 minutes per day on social media apps.

Today the average time spent on social media is 2 hours and 24 minutes today for people aged 16 to 64.

In 2020, the average daily time spent on social media by U.S. users amounted to ** minutes. This duration is set to stay stable in the upcoming years. The daily minutes in 2020 are strongly related to social distancing due to COVID-19 and thus spending more time with social platforms in order to stay connected.

Which countries spent the most and least time on social media?

1) Data Introduction • The Social Media Usage Dataset(Applications) features patterns and activity indicators that 1,000 users use seven major social media platforms, including Facebook, Instagram, and Twitter.

2) Data Utilization (1) Social Media Usage Dataset(Applications) has characteristics that: • This dataset provides different social media activity data for each user, including daily usage time, number of posts, number of likes received, and number of new followers. (2) Social Media Usage Dataset(Applications) can be used to: • Analysis of User Participation by Platform: You can analyze participation and popular trends by platform by comparing usage time and activity for each social media. • Establish marketing strategy: Based on user activity data, it can be used for targeted marketing, content production, and user retention strategies.

Social Media has become a part of our day-to-day routine, keeping users from across the world well-connected through digital platforms. With each passing year, social media is evolving at a rapid speed. With each passing year, the number of social media users is increasing at an immersive speed. Reports also suggest the number of social media users will reach a milestone of 5.85 billion in 2027.

In 2024, 62.6% of the world’s population will access social media, which clearly indicates the dominance of social media platforms in today’s world. In this article, we will examine social media statistics for 2024, uncovering monthly active users, daily time spent by users, most downloaded social media apps, etc.

I’ve compiled a list of the latest social media user statistics showing just how big social media has become and where it’s likely to go in the future.

As of March 2020, social media users in the United States were staying online more. According to a survey of U.S. social media users, **** percent of respondents were using social media *** hours additional hours per day. A further **** percent used social media ** minutes to *** hour more than usual per day. Only *** percent of users were adding ******************** to their usage. Additional social media usage was a result of the coronavirus pandemic, which caused stay home orders and social distancing to be put in place in the country.

The results might surprise you when looking at internet users that are active on social media in each country.

Global ad spend were expected to reach over $134 billion in 2022. This means that it has increased by over 17% yearly.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

📊 Social Media vs Productivity — Realistic Behavioral Dataset (30,000 Users)

This dataset explores how daily digital habits — including social media usage, screen time, and notification exposure — relate to individual productivity, stress, and well-being.

🔍 What’s Inside?

The dataset contains 30,000 real-world-style records simulating behavioral patterns of people with various jobs, social habits, and lifestyle choices. The goal is to understand how different digital behaviors correlate with perceived and actual productivity.

🧠 Why This Dataset is Valuable

• ✅ Designed for real-world ML workflows
  Includes missing values, noise, and outliers — ideal for practicing data cleaning and preprocessing.

• 🔗 High correlation between target features
  The perceived_productivity_score and actual_productivity_score are strongly correlated, making this dataset suitable for experiments in feature selection and multicollinearity.

• 🛠️ Feature Engineering playground
  Use this dataset to practice feature scaling, encoding, binning, interaction terms, and more.

• 🧪 Perfect for EDA, regression & classification
  You can model productivity, stress, or satisfaction based on behavior patterns and digital exposure.

🧾 Columns & Feature Info

📌 Notes

• Contains NaN values in critical columns (productivity, sleep, stress) for data imputation tasks
• Includes outliers in media usage, coffee intake, and notification count
• Target columns are strongly correlated for multicollinearity testing
• Multi-purpose: regression, classification, clustering, visualization

💡 Use Cases

• Exploratory Data Analysis (EDA)
• Feature engineering pipelines
• Machine learning model benchmarking
• Statistical hypothesis testing
• Burnout and mental health prediction projects

📥 Bonus

👉 Sample notebook coming soon with data cleaning, visualization, and productivity prediction!

Teenagers are the 2nd largest group of people affected by social media addiction. Teens ages 13 to 18 years old spend a significant amount of their free time on social media with an average of 3 hours a day.

Hong Kong internet users spent an average of *** hour ** minutes each day on social media, according to a 2024 digital usage survey. YouTube gained the highest attention with monthly usage time averaging ** hours and ** minutes per user, which means that users spent about ** minutes a day on the platform. Facebook came in second with ** hours and ** minutes in the monthly screen time, down by ***** hours. Instagram, on the other hand, up by *** hour to ** hours and ** minutes.

Datasets of Time Series of Social Media Activity. It includes 16.2 million YouTube videos, 0.8 million Usenet threads, 4.6 million Stack-Overflow questions and 70 thousands PLOS ONE papers. This data is used in JM Miotto, EG Altmann, 'Predictability of extreme events in social media', arXiv:1403.3616.

Context: This dataset offers insights into the usage patterns of social media apps for 1,000 users across seven popular platforms: Facebook, Instagram, Twitter, Snapchat, TikTok, LinkedIn, and Pinterest. It tracks various metrics such as daily time spent on the app, number of posts made, likes received, and new followers gained.

Dataset Features:

User_ID: Unique identifier for each user. App: The social media platform being used. Daily_Minutes_Spent: Total time a user spends on the app each day, ranging from 5 to 500 minutes. Posts_Per_Day: Number of posts a user creates per day, ranging from 0 to 20. Likes_Per_Day: Total number of likes a user receives on their posts each day, ranging from 0 to 200. Follows_Per_Day: The number of new followers a user gains daily, ranging from 0 to 50. Context & Use Cases: This dataset could be particularly useful for social media analysts, digital marketers, or researchers interested in understanding user engagement trends across different platforms. It provides insights into how much time users spend, how actively they post, and the level of engagement they receive (in terms of likes and followers).

Conclusion & Outcome: Analyzing this dataset could yield several outcomes:

Engagement Patterns: Identifying which platforms have higher engagement in terms of time spent or likes received. Active Users: Determining which users are the most active across various platforms based on the number of posts and followers gained. User Retention: Studying the correlation between time spent and follower growth, providing insight into user retention strategies for different platforms. Overall, the dataset allows for exploration of social media usage trends and helps drive decision-making for marketing strategies, content creation, and platform engagement.

Snapshot img

Social Media Listening Market Size 2025-2029

The social media listening market size is forecast to increase by USD 4.87 billion at a CAGR of 8.9% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing usage of social media platforms worldwide. With over 4.3 billion users as of 2021, social media has become a powerful tool for businesses to engage with their customers and gain valuable insights into consumer behavior and preferences. A key trend in this market is the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in social media listening solutions, enabling more accurate and efficient data analysis. However, this market is not without challenges. Data privacy and regulatory compliance are becoming increasingly important, with stricter regulations being implemented to protect user data.
Companies must ensure they have strong data security measures in place to comply with these regulations and maintain consumer trust. Additionally, the vast amount of data generated on social media requires sophisticated analytics tools to extract meaningful insights. As such, businesses seeking to capitalize on the opportunities presented by the market must invest in advanced analytics solutions and prioritize data security and privacy. By doing so, they can effectively navigate the challenges and stay ahead of the competition.

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

Request Free Sample

Social media listening has emerged as a crucial business tool, enabling organizations to gain valuable insights from the vast amount of data generated through social media activity. This data is analyzed using techniques such as topic modeling and sentiment scoring to understand consumer behavior, preferences, and trends. Social media geographics and demographics provide essential context, while social media reach and volume measure the scope and impact of conversations. Social media pulse and sentiment reflect the current sentiment and buzz surrounding specific topics, offering real-time insights into market dynamics and trends.
Social media listening software is a vital component of the global market for social media analytics. Social media influence is assessed through the size and engagement of an audience, providing valuable information for marketing and brand management strategies. The social media landscape and heatmap offer a comprehensive view of the social media ecosystem, helping businesses stay informed and adapt to evolving patterns.

How is this Social Media Listening Industry segmented?

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

Type

  Software
  Services


End-user

  Retail and e-commerce
  IT and telecom
  BFSI
  Media and entertainment
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa



  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The software segment is estimated to witness significant growth during the forecast period. This segment encompasses platforms and tools that offer real-time, automated, and scalable capabilities to monitor and analyze social media conversations across various channels such as Twitter, Facebook, Instagram, LinkedIn, TikTok, and Reddit. Real-time monitoring is a key feature of these solutions, empowering brands to identify mentions, trends, and sentiment as they emerge. By staying abreast of evolving topics, businesses can respond promptly to customer concerns, capitalize on viral events, and maintain a strong online presence. Artificial Intelligence (AI) and Machine Learning (ML) technologies are integral to social media listening software, enabling advanced topic identification, sentiment analysis, and trend recognition.

These technologies enable businesses to gain valuable customer insights, inform product development, and enhance customer experience. Social media listening platforms also offer data visualization and reporting features, allowing businesses to analyze and present their findings in a clear and actionable manner. Additionally, they provide social media dashboards, alerts, and governance tools to ensure compliance with social media policies and ethical standards. In summary, social media listening software plays a pivotal role in the global market for social media analytics, offering real-time insights and advanced capabilities to help businesses navigate the complex social media landscape and engage effectively with their audience.

Get a glance at the market report of share

Twitter user statistics show a varying degree of how often users login to the platform. Here’s what it looks like.