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.

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.

This is dummy data, that I have generated by using the 'NumPy' Library of Python. This data shows how much a user spends time on their devices using Social Media.

I generated this data to train an AI model for myself for practice purposes only.

The description for each column is as follows:

• age: The age of the user.
• gender: The gender identity of the user (Male, Female, Non-binary).
• demographics: The type of area the user resides in (Urban, Suburban, Rural).
• interests: The user's primary area of interest or hobby.
• device_type: The type of device used by the user (Mobile).
• location:The country of residence for the user.
• platform: The social media platform where the user spends time.
• profession: The user's occupation or professional status.
• income: The yearly income of the user.
• indebt: Indicates whether the user is in debt (True or False).
• homeowner: Indicates whether the user owns a home (True or False).
• owns_cars: Indicates whether the user owns cars (True or False).

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              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 lesser 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. Mobile-first market 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.

In 2008, the average human attention span was 12 seconds. Fast forward to 2025, and many studies suggest it's now hovering around 8 seconds, shorter than that of a goldfish. It’s no coincidence that during this same period, social media platforms surged to dominate how we consume content. Whether you're...

📊 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!

During the third quarter of 2024, internet users in the United Kingdom (UK) spent an average over five and a half hours per day accessing the internet via any device. UK online audiences spent approximately * hour and ** minutes per day on social media, while an additional ** minutes were devoted to using a gaming console daily. Traditional media - is usage declining? Some forms of traditional media in the country are in decline more than others when it comes to usage, which is especially visible in the case of TV. In recent years the number of TV households in the UK has remained fairly unchanged; however, the time spent watching television has been constantly decreasing. Interestingly, radio listenership has not followed this trend. Digital radio format usage is growing as per some studies. Yet, live radio dominates the time spent with any audio in the UK by a large margin.British radio is a showcase for the loyalty of its fans. In mid-2023, after 30 years of employment at BBC, Ken Bruce, one of the highest-earning presenters of the company, left to work at a rival radio station. He took both of his signature programs to his new employer. Even though BBC Radio 2 remains the leading radio station in the UK by audience numbers, Bruce’s departure meant a loss of * million listeners for the BBC between the first and second quarters of 2023.

The global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.

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 global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, 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.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).

this graph was create in Power Bi:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F13cc72b2c805991d7af10ea6aa396cd0%2FSem%20ttulo_page-0001.jpg?generation=1710364280999487&alt=media" alt="">

Understanding Global Work Patterns: A Deep Dive into Working Hours Across Countries and Over Time

Introduction

Work, an integral part of human life, has undergone significant transformations over the past century and a half. The amount of time individuals dedicate to work has shifted, reflecting changes in societal norms, economic structures, and technological advancements. This exploration delves into the intricate dynamics of working hours worldwide, shedding light on disparities across countries and within societies. By examining historical trends and contemporary data, we gain insights into the evolving nature of work and its profound impact on individuals' lives.

Historical Context

The Industrial Revolution marked a pivotal moment in human history, fundamentally altering the nature of work. With the mechanization of industries, the concept of the traditional workday emerged, characterized by long hours and minimal breaks. Throughout the 19th and early 20th centuries, workers endured grueling schedules, often exceeding 12 hours per day, six days a week. This relentless pursuit of productivity came at the expense of worker well-being and family life, prompting calls for labor reforms.

Labor Movements and Reform

The rise of labor movements in the late 19th and early 20th centuries sparked a wave of social change, advocating for shorter workdays and improved working conditions. The landmark achievements, such as the eight-hour workday and weekends off, marked significant milestones in the fight for workers' rights. Countries worldwide implemented labor laws to regulate working hours, aiming to strike a balance between economic productivity and human welfare. These reforms laid the foundation for the modern workweek and paved the way for further advancements in labor standards.

Contemporary Work Patterns

In the 21st century, the landscape of work continues to evolve, shaped by globalization, technological innovation, and shifting societal values. While many industrialized nations have embraced shorter workweeks and increased leisure time, disparities persist on a global scale. Developed countries typically exhibit lower average working hours, accompanied by robust social welfare systems and flexible labor policies. In contrast, developing economies often grapple with longer work hours, driven by economic necessity and informal employment practices.

Regional Disparities

Regional variations in working hours highlight the complex interplay of cultural, economic, and political factors. In Europe, countries like France and Germany have embraced a culture of work-life balance, with statutory limits on working hours and generous vacation entitlements. Scandinavian nations, renowned for their progressive social policies, prioritize employee well-being through initiatives such as flexible work arrangements and parental leave. In contrast, regions like Asia and the Middle East experience longer work hours, influenced by cultural norms emphasizing diligence and dedication.

Gender Dynamics

Gender disparities in working hours remain a persistent challenge, reflecting entrenched inequalities in the workplace. Women often shoulder disproportionate caregiving responsibilities, leading to reduced participation in the labor force and truncated career trajectories. The gender pay gap further exacerbates these disparities, perpetuating a cycle of economic disadvantage for women. Addressing gender inequities in working hours requires multifaceted interventions, including affordable childcare, parental leave policies, and workplace diversity initiatives.

The Gig Economy and Flexible Work The rise of the gig economy and remote work arrangements has reshaped traditional notions of employment and working hours. Freelancers and independent contractors enjoy greater flexibility in scheduling, blurring the boundaries between work and personal life. Digital platforms have facilitated the emergence of remote work opportunities, enabling individuals to customize their work hours and locations. However, concerns persist regarding job security, benefits coverage, and the erosion of traditional labor protections in the gig economy.

Impact on Well-being

The relationship between working hours and well-being is complex, influenced by factors such as job satisfaction, socioeconomic status, and work-life balance. While longer work hours may boost productivity in the short term, they can lead to burnout, stress, and diminished quality of life over time. Conversely, shorter workweeks and increased leisure time have been linked to improved mental health, greater h...

Survey of 350 businesses on how much they spend on social media management and their social media management costs

Pandemic has influenced all spheres of the humanity. COVID-19 impacted the education vertical in larger manner. Traditional classroom environment plays a very vital role in molding the life of an individual. Bond nurtured in the early ages of the life acts as the great moral support in the latter stages of the journey. As the pandemic has forced us into online education, this data collection aims to analyze the impact of online education. To check out the satisfactory level of the learners, review was conducted.

Gender – Male, Female Home Location – Rural, Urban Level of Education – Post Graduate, School, Under Graduate Age – Years Number of Subjects – 1- 20 Device type used to attend classes – Desktop, Laptop, Mobile Economic status – Middle Class, Poor, Rich Family size – 1 -10 Internet facility in your locality – Number scale (Very Bad to Very Good) Are you involved in any sports? – Yes, No Do elderly people monitor you? – Yes, No Study time – Hours Sleep time – Hours Time spent on social media – Hours Interested in Gaming? – Yes, No Have separate room for studying? – Yes, No Engaged in group studies? – Yes, No Average marks scored before pandemic in traditional classroom – range Your interaction in online mode - Number scale (Very Bad to Very Good) Clearing doubts with faculties in online mode - Number scale (Very Bad to Very Good) Interested in? – Practical, Theory, Both Performance in online - Number scale (Very Bad to Very Good) Your level of satisfaction in Online Education – Average, Bad, Good

radhakrishnan, sujatha (2021), “Online Education System - Review”, Mendeley Data, V1, doi: 10.17632/bzk9zbyvv7.1

Context

This data set has been sourced from the Machine Learning Repository of University of California, Irvine (UC Irvine) : Travel Review Ratings Data Set. This data set is populated by capturing user ratings from Google reviews. Reviews on attractions from 24 categories across Europe are considered. Google user rating ranges from 1 to 5 and average user rating per category is calculated.

Content

Attribute 1 : Unique user id Attribute 2 : Average ratings on churches Attribute 3 : Average ratings on resorts Attribute 4 : Average ratings on beaches Attribute 5 : Average ratings on parks Attribute 6 : Average ratings on theatres Attribute 7 : Average ratings on museums Attribute 8 : Average ratings on malls Attribute 9 : Average ratings on zoo Attribute 10 : Average ratings on restaurants Attribute 11 : Average ratings on pubs/bars Attribute 12 : Average ratings on local services Attribute 13 : Average ratings on burger/pizza shops Attribute 14 : Average ratings on hotels/other lodgings Attribute 15 : Average ratings on juice bars Attribute 16 : Average ratings on art galleries Attribute 17 : Average ratings on dance clubs Attribute 18 : Average ratings on swimming pools Attribute 19 : Average ratings on gyms Attribute 20 : Average ratings on bakeries Attribute 21 : Average ratings on beauty & spas Attribute 22 : Average ratings on cafes Attribute 23 : Average ratings on view points Attribute 24 : Average ratings on monuments Attribute 25 : Average ratings on gardens

Acknowledgements

This data set has been sourced from the Machine Learning Repository of University of California, Irvine (UC Irvine) : Travel Review Ratings Data Set

The UCI page mentions the following publication as the original source of the data set: Renjith, Shini, A. Sreekumar, and M. Jathavedan. 2018. Evaluation of Partitioning Clustering Algorithms for Processing Social Media Data in Tourism Domain. In 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 12731. IEEE

Inspiration

I'm kind of people who love traveling. But sometimes I've problems like where should I visit? Are there somewhere interesting places matched with my lifestyle? Often I spent hours to search for interesting place to go out. Such a waste of time.

What if we can build a recommender system which can recommend you several interesting venue based on your preferences. With information from Google review, I'll try to divide Google review user into cluster of similar interest for further work of building recommender system based on thier preference.

Survey of 620 businesses on social media marketing pricing

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is the Consumer Goods Index a Reliable Barometer of American Spending?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

As of the third quarter of 2024, internet users in South Africa spent more than **** hours and ** minutes online per day, ranking first among the regions worldwide. Brazil followed, with roughly **** hours of daily online usage. As of the examined period, Japan registered the lowest number of daily hours spent online, with users in the country spending an average of over **** hours per day using the internet. The data includes the daily time spent online on any device. Social media usage In recent years, social media has become integral to internet users' daily lives, with users spending an average of *** minutes daily on social media activities. In April 2024, global social network penetration reached **** percent, highlighting its widespread adoption. Among the various platforms, YouTube stands out, with over *** billion monthly active users, making it one of the most popular social media platforms. YouTube’s global popularity In 2023, the keyword "YouTube" ranked among the most popular search queries on Google, highlighting the platform's immense popularity. YouTube generated most of its traffic through mobile devices, with about 98 billion visits. This popularity was particularly evident in the United Arab Emirates, where YouTube penetration reached approximately **** percent, the highest in the world.

The impact of smartphones and social media on mental health depends on how people use these technologies. Research distinguishes between overall, social and non-social smartphone use, as well as between overall, active, and passive social media use. Recent studies revealed that the consequences of these usage types for mental health may depend on personality traits; however, evidence remains elusive. This study examines the moderating role of extraversion and neuroticism in the within-person relationship between different types of (a) smartphone use and mental health, and (b) Instagram use and mental health. In a weeklong diary study with 142 participants (63% female, average age = 26.39), we collected data on subtypes of smartphone and Instagram use, and positive and negative affect. Neuroticism and extraversion were measured at baseline. Passive Instagram use predicted increases in negative affect. Moreover, Neuroticism (but not extraversion) emerged as a significant moderator. Only for users scoring high on neuroticism, time spent on smartphones and passive use of Instagram predicted increases in negative affect, emphasizing the complex interplay between usage types, user characteristics, and mental health. These findings may help practitioners tailor interventions to specific populations. Future research with diverse samples is necessary to identify additional vulnerability and protective user characteristics.

Context

LinkedIn is a place for increasing connection, showing your skills and achievements. Therefore in order to understand the various features like promotions, regional analysis and facial characteristics. This data is taken into consideration.

Content

Data is consisting of around 15000 profiles. The data set deals with a lot of features like region, the way the images are being uploaded, the emotions on them and growth of the users over time.

Lets understand the following attributes for the betterment:-

User id is a thing of privacy and should not be disclosed although there characteristics can be given in order to understand the various behavior pattern of people in LinkedIn. c id : name for each data, basically forms the primary key.

Profession Columns avg time in previous position: The amount of time spent in years in the previous position avg current position length: The amount of time on an average the user is present in the current position avg previous position length: The amount of time on an average the user is present in the previous position m urn: The user id for each profile m urn id: This is reduced to a distinct code no of promotions: Total number of times the user was promoted no of previous positions: The number of previous positions the user holds current position length: The number of months the person is in current position age: The Age of the person gender: Male or Female ethnicity: The percentage of ethnicity n followers: Number of followers

Image Clarity beauty: The beauty is the index for the analysis of the beauty female: This predicts the user image is more to be female or not.
beauty male: This predicts the user image is more to be male or not. blur: The degree of shadiness of the image

Emotion Captured emo anger: The percentage of anger found emo disgust: The percentage of disgust found emo fear : The percentage of fear found emo happiness: The percentage of happiness found emo neutral: The percentage of neutral emo sadness: The percentage of sadness emo surprise: The percentage of surprise

Orientation & Facial Accessories glass: The person is wearing glasses or not or sunglasses head pitch: The orientation of head(basically Up or down) head roll: The orientation of head(side ways rolling; horizontal or vertical) head yaw: The orientation of head(side facing; left or right) mouth close: The percentage of closed mouth mouth mask: The percentage of masked mouth mouth open: The percentage of open mouth mouth other: The percentage of other mouth things skin acne: The percentage of skin tone skin dark_circle: The percentage of dark circle on skin skin health: The growth of the skin percentage skin stain: The stain percentage on skin smile: The smile percentage

Region Columns nationality: The nationality belonging Followed by the percentage of each:- african celtic english
east asian
european
greek
hispanic
jewish
muslim
nordic
south asian

face_quality: The quality of the face recognized.

Acknowledgements

We wouldn't be here without the help of Kagglers. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Always wanted to contribute to the data science community and open up to questions.

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.