According to a survey conducted in June 2023, adults in the United States spent more time per day on TikTok than on any other leading social media platform. Overall, respondents reported spending an average of 53.8 minutes per day on the social video app. YouTube and Twitter ranked second and third, each with an average of 48 minutes and 34 minutes spent on the platforms per day, respectively.

U.S. teens have time for certain platforms

Different social media platforms attract different demographics, with teenagers in the United States being more drawn to TikTok and YouTube over Facebook. In 2023, teenagers in the United States spent an average of almost two hours on YouTube and 1.5 hours on TikTok every day, 1451257 while Facebook was used by teens for less than half an hour per day. Furthermore, social media habits differ between genders, as teen girls were more likely to spend more time than boys on Instagram.

TikTok is king for teens and Gen Z

Although spending 1.5 hours on the Generation Z app of choice may sound rather modest, some TikTok users devote much more of their time to the platform . According to a survey conducted in the United States in 2022, around eight percent of teenagers in the United States spent over five hours a day on TikTok. 1417187 whereas another 22 percent reported spending between two and three hours daily on the video-based app.

As of February 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 usage Currently, 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 and friends. Global impact of social media Social 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 polarization in politics, and heightened everyday distractions.

It starts with a familiar flick of the thumb. A notification pops up during breakfast, a reel plays in the background while brushing teeth, and before we know it, half the morning has disappeared into a scroll. This isn’t just anecdotal, it’s a digital behavior woven into the daily routine...

Dataset

This dataset was created by SAGAR KARAR

Released under CC0: Public Domain

Contents

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 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 three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two 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.

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 ...

In February 2024, adults in the United States aged between 18 and 24, spent 186 minutes per day engaging with social media platforms. In comparison, respondents aged 65 and older dedicated approximately 102 minutes of their day to social media. TikTok was the most engaging social media platform for U.S. consumers aged between 18 and 24 years. The popular video was also the most engaging among users aged 35 and 54 years, commanding between 45 and 50 minutes of users' daily attention. Respondents aged between 55 and 65, reported to spending between 45 minutes daily on Facebook.

During the second quarter of 2025, internet users in Kenya spent the most time per week using social media, excluding watching videos, at 14 hours and 18 minutes. The Philippines and Nigeria also reported high usage levels. By comparison, internet users in Japan spent less than *********** per week on social media. Low levels of daily usage were also recorded in China and South Korea. As of February 2025, the social network Facebook was ranked first worldwide in terms of active users, at over ***** billion. Other popular social media include mobile messaging platforms YouTube and WhatsApp, as well as social content sharing networks such as Instagram and social video platform TikTok. Most social networks are accessible through multiple platforms, but many popular social media started out as mobile apps, demonstrating the growing trend of mobile-first development. Examples include Instagram, which initially was launched as an iOS photo editing and discovery app as well as a mobile social messenger, and TikTok. Social networking does not only enable users to connect with other people but also with brands and celebrities. Social media has also become a growing source of news for internet users in many countries.

A curated dataset of 2025 social media statistics including global user identities, adoption rates, daily time spent, reasons for use, platforms per month, and platform ad reach.

The number of hours spent on social media in Sweden has fluctuated over the past few years. Swedish users spent around two hours and one minute every day on social media as of February 2025, a marginal increase from the previous year. In 2021, an average of one hour and 48 minutes were spent on social networks per day, rising to over two hours in 2022.

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

In the third quarter of 2024, on average, Vietnamese internet users spent * hours and ** minutes using social media on all devices. In that year, Facebook was the leading active social media apps in the country.

How many people use social media? Social media usage is one of the most popular online activities. In 2025, over 5.4 billion people were estimated to be using social media worldwide, a number projected to increase to over 6.6 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 59 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 151 minutes per day on social media and messaging apps, an increase of 40 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 one billion registered accounts and currently boasts approximately 2.9 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.

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

Impact of Social Media on Suicide Rates: Produced Results

Overview

This dataset explores the impact of social media usage on suicide rates, presenting an analysis based on social media platform data and WHO suicide rate statistics. It is an insightful resource for researchers, data scientists, and analysts looking to understand the correlation between increased social media activity and suicide rates across different regions and demographics.

Content

The dataset includes the following key sources:

WHO Suicide Rate Data (SDGSUICIDE): Retrieved from WHO data export, which tracks global suicide rates. Social Media Usage Data: Information from major social media platforms, sourced from Kaggle, supplemented with data from:

Facebook: Statista

Twitter: Twitter Investor Relations

Instagram: Facebook Investor Relations

Acknowledgements

We would like to acknowledge:

World Health Organization (WHO): For providing global suicide rate data, accessible under their data policy (WHO Data Policy). Kaggle Dataset Contributors: For social media usage data that played a crucial role in the analysis.

Usage

This dataset is useful for studying the potential social factors contributing to suicide rates, especially the role of social media. Analysts can explore correlations using time-series analysis, regression models, or other statistical tools to derive meaningful insights. Please ensure compliance with the Creative Commons Attribution Non-Commercial Share Alike 4.0 International License (CC BY-NC-SA 4.0).

Data Files

Impact-of-social-media-on-suicide-rates-results-1.1.0.zip (90.9 kB) Contains processed results and supplementary data.

Citations

If you use this dataset in your work, please cite:

Martin Winkler. (2021). Impact of social media on suicide rates: produced results (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4701587 https://zenodo.org/records/4701587

License

This dataset is released under the Creative Commons Attribution Non-Commercial Share Alike 4.0 International (CC BY-NC-SA 4.0) license. You are free to share and adapt the material, provided proper attribution is given, it's not used for commercial purposes, and any derivatives are distributed under the same license.

Columns

Year: The year of the recorded data. Sex: Demographic indicator (e.g., male, female). Suicide Rate % Change Since 2010: Percentage change in suicide rates compared to the year 2010. Twitter User Count % Change Since 2010: Percentage change in Twitter user counts compared to the year 2010. Facebook User Count % Change Since 2010: Percentage change in Facebook user counts compared to the year 2010.

Data Bins

The dataset includes categorized data ranges, allowing for analysis of trends within specified intervals. For example, ranges for suicide rates, Twitter user counts, and Facebook user counts are represented in bins for better granularity.

Count Summary

The dataset summarizes counts for various intervals, enabling researchers to identify trends and patterns over time, highlighting periods of significant change or stability in both suicide rates and social media usage.

Use Cases

This dataset can be used for:

Statistical analysis to understand correlations between social media usage and mental health outcomes. Academic research focused on public health, psychology, or sociology. Policy-making discussions aimed at addressing mental health concerns linked to social media.

Cautions

The dataset contains sensitive information regarding suicide rates. Users should handle this data with care and sensitivity, considering ethical implications when presenting findings.

In 2023, adults in the United States spent a total of *** billion minutes on Facebook per day, making the social network the most popular platform in terms of daily user engagement. However, this is set to change as TikTok is projected to overtake the blue giant in 2025 as Facebook's daily usage time is projected to decline by then.

Sources: Kaggle This dataset, created using NumPy and Pandas, mimics real-world social media usage patterns for research and analysis through synthetic data generation techniques.

Collection Methodology

About Dataset The "Time-Wasters on Social Media" dataset provides a comprehensive insight into user interactions and engagement with various social media platforms. This dataset encompasses a wide range of attributes that facilitate a thorough analysis of how social media affects users' time management and productivity. It serves as an essential resource for researchers, marketers, and social scientists who seek to delve into the intricacies of social media consumption patterns.

Generated through advanced synthetic data techniques using tools like NumPy and pandas, this dataset mimics real-world social media usage scenarios. Despite being artificially created, it accurately reflects genuine usage trends, making it a valuable asset for conducting research and analysis in the realm of social media behavior.

Columns Description UserID: Unique identifier assigned to each user. Age: The user's age. - Gender: The user's gender (e.g., male, female, non-binary). Location: Geographic location of the user. Income: The user's income level. Debt: Amount of debt the user has. Owns Property: Indicates whether the user owns property. Profession: The user's occupation or job. Demographics: Statistical data about the user (e.g., age, gender, income). Platform: The platform the user is using (e.g., website, mobile app). Total Time Spent: The total time the user spends on the platform. Number of Sessions: The number of times the user has logged into the platform. Video ID: Unique identifier for a video. Video Category: The category or genre of the video. Video Length: Duration of the video. Engagement: User interaction with the video (e.g., likes, comments, shares). Importance Score: A score indicating how important the video is to the user. Time Spent On Video: The amount of time the user spends watching a video. Number of Videos Watched: The total number of videos watched by the user. Scroll Rate: The rate at which the user scrolls through content. Frequency: How often the user engages with the platform. Productivity Loss: The impact of platform usage on the user's productivity. Satisfaction: The user's satisfaction level with the platform or content. Watch Reason: The reason why the user is watching a video (e.g., entertainment, education). Device Type: The type of device the user is using (e.g., smartphone, tablet, desktop). OS: The operating system of the user's device (e.g., iOS, Android, Windows). Watch Time: The time of day when the user watches videos. Self Control: The user's ability to control their usage of the platform. Addiction Level: The user's level of dependency on the platform. Current Activity: What the user is doing while watching the video. Connection Type: The type of internet connection the user has (e.g., Wi-Fi, cellular).

In the last two decades, social media usage has surged, reaching nearly five billion users worldwide in 2022. Unfortunately, there is a rise in mental health issues during that same time. Through a two-phase data analysis, this project studies the patterns of mental health influenced by social media. Analyzing data from 479 individuals across various platforms, the study employs K-means clustering to categorize mental health states into three groups, each indicating varying levels of professional/intervention needs. In the subsequent supervised learning phase, predictive models, including the Naive Bayes model with an under-sampled dataset and the Decision Tree model with an oversampled dataset, were developed to determine mental health categories, achieving an accuracy of 60.42%. These models, developed with comprehensive predictors, offer valuable insights for future research and the need for interventions addressing mental health challenges linked to social media use. Table 1 displays the variables, their descriptions, and value types. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2Fd9e0fb90d862e58aba958a14b3b8dcea%2FScreen%20Shot%202023-12-14%20at%2012.27.20%20PM.png?generation=1702578478575969&alt=media" alt="">

Phase I : Unsupervised Learning Techniques K-means Clustering Model

Using the elbow method pictured below in plot 1, we could visualize the optimal number of clusters (K), and then perform the K-means clustering with the optimal K. Several values for K were considered, and models were created for K = 2, 3, 4, 5, 6, 7, and 8, which were then compared. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2Fa77706842d108c7fbee363c1192b763a%2FScreen%20Shot%202023-12-14%20at%2012.08.01%20PM.png?generation=1702577407983039&alt=media" alt="">

In table 4 we can see the comparison of the bss/tss ratios. K = 3 is the last model with a significant jump and therefore is the optimal model. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2F9a44382d9c08a616bd0248f150b85526%2FScreen%20Shot%202023-12-14%20at%2012.08.20%20PM.png?generation=1702577436944201&alt=media" alt="">

In Table 5, we can observe the cluster centers for each variable within each cluster in the K-means clustering model with k = 3.https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2Fdf92bc28b65f67d88efa3b8a96295dcc%2FScreen%20Shot%202023-12-14%20at%2012.09.13%20PM.png?generation=1702577557552624&alt=media" alt="">

Based on the above cluster centers, we could interpret the cluster groups as shown in the table 6 below: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2F1d0624052cfc9ce50e7bc5b404d916d0%2FScreen%20Shot%202023-12-14%20at%2012.08.34%20PM.png?generation=1702577449886328&alt=media" alt="">

Phase II: Supervised Learning Techniques

Prediction Models

Data Input https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2F51672c4d16a801532a3ac8017cf72958%2FScreen%20Shot%202023-12-14%20at%2012.16.16%20PM.png?generation=1702577897888133&alt=media" alt=""> Above in Image A, we can see a sneak peek of the dataset with the new variable 'MHScore,' indicating mental health state cluster groups.

The outcome variable (MHScore) is categorical and multi-class (3 Levels: 1,2,3). Therefore, the implemented models include Naïve Bayes (NB), Support Vector Machines (SVM), SVM with parameter changes, Decision Trees, and Pruned Decision Trees.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2F06827fe209b78ffbddee69b272a8cdfc%2FScreen%20Shot%202023-12-14%20at%2012.20.41%20PM.png?generation=1702578062241650&alt=media" alt="">

Table 11 summarizes the results of the best model from each predictive machine learning technique for accuracy, balanced accuracy, sensitivity, specificity, and precision for each class. Each model was developed using the same predictors from the dataset, including age, gender, relationship status, occupation, organization of employment, social media usage, the number of social media platforms used, the hours spent on social media, and the frequency of social media use. The higher accuracy observed in both the under-sampled and oversampled datasets indicates the importance of class equality.

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.

According to a survey conducted in Japan in fiscal year 2024, people on average spent **** minutes per weekday using social media. The usage time decreased compared to the previous year due to the inclusion of an additional age group in the survey.