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.

Facebook is fast approaching 3 billion monthly active users. That’s about 36% of the world’s entire population that log in and use Facebook at least once a month.

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

56.8% of the world’s total population is active on social media.

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

Context

This dataset explores the relationship between digital behavior and mental well-being among 100,000 individuals. It records how much time people spend on screens, use of social media (including TikTok), and how these habits may influence their sleep, stress, and mood levels.

It includes six numerical features, all clean and ready for analysis, making it ideal for machine learning tasks like regression or classification. The data enables researchers and analysts to investigate how modern digital lifestyles may impact mental health indicators in measurable ways.

Dataset Applications

• Quantify how screen‑time, TikTok use, or multi‑platform engagement statistically relate to stress, sleep loss, and mood.
• Train regression or classification models that forecast stress level or mood score from real‑time digital‑usage metrics.
• Feed user‑specific data into recommender systems that suggest screen‑time caps or bedtime routines to improve mental health.
• Provide evidence for guidelines on youth screen‑time limits and platform moderation based on observed stress‑sleep trade‑offs.
• Serve as a teaching dataset for EDA, feature engineering, and model evaluation in data‑science or psychology curricula.
• Evaluate app interventions (e.g., screen‑time nudges) by comparing predicted versus actual post‑intervention stress or mood shifts.
• Cluster individuals into digital‑behavior personas (e.g., “heavy late‑night scrollers”) to tailor mental‑health resources.
• Generate synthetic time‑series scenarios (what‑if reductions in TikTok hours) to estimate downstream impacts on sleep and stress.
• Use engineered features (ratio of TikTok hours to total screen‑time, etc.) in broader wellbeing models that include diet or exercise data.
• Assess whether mental‑health prediction models remain accurate and unbiased across different screen‑time or platform‑use segments. # Column Descriptions
• screen_time_hours – Daily total screen usage in hours across all devices.
• social_media_platforms_used – Number of different social media platforms used per day.
• hours_on_TikTok – Time spent on TikTok daily, in hours.
• sleep_hours – Average number of sleep hours per night.
• stress_level – Stress intensity reported on a scale from 1 (low) to 10 (high).
• mood_score – Self-rated mood on a scale from 2 (poor) to 10 (excell # Inspiration This dataset was inspired by growing concerns about how screen time and social media affect mental health. It enables analysis of the links between digital habits, stress, sleep, and mood—encouraging data-driven solutions for healthier online behavior and emotional well-being. # Ethically Mined Data: This dataset has been ethically mined and synthetically generated without collecting any personally identifiable information. All values are artificial but statistically realistic, allowing safe use in academic, research, and public health projects while fully respecting user privacy and data ethics.

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

Gen Z and Millennials are the biggest social media users of all age groups.

The number of Reddit users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.3 million users (+5.21 percent). After the ninth consecutive increasing year, the Reddit user base is estimated to reach 208.12 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Reddit users in countries like Mexico and Canada.

📊 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!

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

In this post, I'll give you all the social media addiction statistics you need to be aware of to moderate your social media use.

The number of social media users in the United States was forecast to continuously increase between 2024 and 2029 by in total 26 million users (+8.55 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 330.07 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The average person has 8-9 social media accounts. This has doubled since 2013, when the average person just had 4-5 accounts.

Context

Facebook is a company that literally every kid is aware of. Its a household name. People from various age groups are there on this social media website. It has helped many in connecting with different people and also has helped some of the investors by earning them a good amount of money. This data set contains the details of the stock of Facebook Inc.

Content

This data set has 7 columns with all the necessary values such as opening price of the stock, the closing price of it, its highest in the day and much more. It has date wise data of the stock starting from 2012 to 2020(August).

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

🧠 About the Dataset This dataset was created to explore and analyze behavioral patterns that may help predict an individual's personality type Introvert or Extrovert based on lifestyle and social interaction metrics. It simulates responses from individuals with varying social habits, preferences, and psychological tendencies.

Each row represents an individual's self-reported behavior, with features focusing on their social energy, online activity, and personal tendencies.

📊 Features Time_spent_Alone (int) – Average number of hours spent alone per day.

Stage_fear (Yes/No) – Whether the individual experiences fear or anxiety when speaking or performing in front of an audience.

Social_event_attendance (int) – Number of social events attended per month.

Going_outside (int) – Number of days per week the individual goes outside for leisure or social activities.

Drained_after_socializing (Yes/No) – Whether the individual feels mentally or emotionally drained after socializing.

Friends_circle_size (int) – Estimated number of close friends or regular companions.

Post_frequency (int) – Number of personal social media posts per month.

Personality (Introvert/Extrovert) – The target variable indicating the individual's personality classification.

🔍 Use Case This dataset is ideal for building supervised classification models (e.g., logistic regression, decision trees, random forest, etc.) to predict personality types. It also supports feature importance analysis, exploratory data visualization, and psychological behavioral profiling.

In 2023, Meta Platforms had a total annual revenue of over 134 billion U.S. dollars, up from 116 billion in 2022. LinkedIn reported its highest annual revenue to date, generating over 15 billion USD, whilst Snapchat reported an annual revenue of 4.6 billion USD.

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.

In this day and age, people face a lot of stress due to the fast pace of life. Due to this, people in today's digital age, suffer from a plethora of ailments. It is universally accepted that a greater awareness of ailments and their corresponding symptoms leads to an increased lifespan and better quality of life. Early detection and screening can help doctors nip diseases in their natal stages. However, not everyone is aware of them, which makes it a global issue. The study of the degree of disease awareness amongst people belonging to different nations and continents is a matter of great interest. One method that is suitable for this purpose is using clinical data. But, this data is not readily available. However, today a plethora of platforms are available to people to share their thoughts and experiences. People post about many of the important events in their lives on social media. Their posts offer a microscopic view into their lives and thought processes. Based on this intuition, twitter data pertaining to various chronic and acute diseases has been collected. Tweets for 30 deadly ailments have been collected over a period of 3 months amounting to a total of 19 million. A feature extraction approach is proposed which is used to identify the disease awareness levels across different nations. Deriving the global awareness landscape for ailments can help to identify regions which are well aware and also those that need to get aware. Clustering has been used for this purpose.