We surveyed 10,208 people from more than 15 countries on their mobile app usage behavior. The countries include USA, China, Japan, Germany, France, Brazil, UK, Italy, Russia, India, Canada, Spain, Australia, Mexico, and South Korea. We asked respondents about: (1) their mobile app user behavior in terms of mobile app usage, including the app stores they use, what triggers them to look for apps, why they download apps, why they abandon apps, and the types of apps they download. (2) their demographics including gender, age, marital status, nationality, country of residence, first language, ethnicity, education level, occupation, and household income (3) their personality using the Big-Five personality traits This dataset contains the results of the survey.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

Table from the American Community Survey (ACS) 5-year series on age and gender related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B01001 Sex by Age, B01002 Median Age by Sex. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B01001, B01002Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estima

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.

Percentage of Canadians who have experienced selected personal effects in their life because of the Internet and the use of social networking websites or apps, during the past 12 months.

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.

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles’ metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen’s d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as “school” for youth and “college” for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.

This dataset presents a comprehensive survey of ride-hailing app users in Pakistan, capturing their experiences, preferences, and behavior regarding these services. With the increasing reliance on digital transportation solutions, ride-hailing apps have transformed urban mobility in the country. This dataset aims to provide insights into how users interact with these services, what factors influence their choices, and how satisfied they are with their overall experience.

The dataset includes key variables such as demographic details (age, gender, occupation), ride frequency, preferred ride-hailing apps, pricing perceptions, and service quality evaluations. Additionally, it explores factors like waiting time, ride availability, safety concerns, and customer support satisfaction. Understanding these elements is crucial for identifying gaps in service and improving user experience.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F24002135%2Fc3d44ebc9e78fc4ffd60bdfce2dc261a%2FDiscovering-Essential-Travel-and-Transport-Android-Apps-in-Pakistan.jpg?generation=1742482903424771&alt=media" alt="">

Researchers, data analysts, and industry professionals can leverage this dataset to study market trends, assess customer satisfaction, and explore areas for service enhancement. It can also be used for predictive modeling, sentiment analysis, and business strategy development in the ride-hailing industry. Policymakers and urban planners may find it useful for transportation planning and infrastructure development.

This dataset is ideal for exploring consumer behavior, evaluating competition among ride-hailing services, and identifying the key drivers behind customer retention and loyalty. Whether you're conducting academic research, working on a business case study, or developing a machine-learning model, this dataset offers valuable insights into the evolving landscape of ride-hailing in Pakistan.

Percentage of Internet users who have experienced selected personal effects in their life because of the Internet and the use of social networking websites or apps, during the past 12 months.

Effectiveness of technology-based interventions to improve physical activity (PA) in people with COPD is controversial. Mixed results may be due to participants' characteristics influencing their use of and engagement with mobile health apps. This study compared demographic, clinical, physical and PA characteristics of patients with COPD using and not using mobile apps in daily life. Patients with COPD who used smartphones were asked about their sociodemographic and clinic characteristics, PA habits and use of mobile apps (general and PA-related). Participants performed a six-minute walk test (6MWT), gait speed test and wore an accelerometer for 7 days. Data were compared between participants using (App Users) and not using (Non-App Users) mobile apps. A sub-analysis was conducted comparing characteristics of PA–App Users and Non-Users. 59 participants were enrolled (73% Male; 66.3 ± 8.3 yrs; FEV1 48.7 ± 18.4% predicted): 59% were App Users and 25% were PA-App Users. Significant differences between App Users and Non-App Users were found for age (64.2 ± 8.9 vs. 69.2 ± 6.3yrs), 6MWT (462.9 ± 91.7 vs. 414.9 ± 82.3 m), Gait Speed (Median 1.5 [Q1–Q3: 1.4–1.8] vs. 2.0 [1.0–1.5]m/s), Time in Vigorous PA (0.6 [0.2–2.8] vs. 0.14 [0.1–0.7]min) and Self-Reported PA (4.0 [1.0–4.0] vs. 1.0 [0.0–4.0] Points). Differences between PA–App Users and Non-Users were found in time in sedentary behavior (764.1 [641.8–819.8] vs. 672.2 [581.2–749.4] min) and self-reported PA (4.0 [2.0–6.0] vs. 2.0 [0.0–4.0] points). People with COPD using mobile apps were younger and had higher physical capacity than their peers not using mobile apps. PA-App Users spent more time in sedentary behaviors than Non-Users although self-reporting more time in PA.

let's break down each column in this fitness tracker app data:

1. UserID: This column contains unique identifiers for each user of the fitness tracker app. Each row corresponds to a specific user's data.

2. Date: This column represents the date on which the data was recorded or collected. It's likely in a date format (e.g., YYYY-MM-DD).

3. Steps: This column records the number of steps the user took on the given date. Steps are a common metric used by fitness trackers to measure physical activity.

4. Total_Distance: This column indicates the total distance covered by the user on the given date, likely measured in a unit such as kilometers or miles. It might be calculated based on steps taken and stride length.

5. Tracker_Distance: This column represents the distance recorded by the fitness tracker device itself, which could include steps as well as other factors like GPS data.

6. Logged_Activities_Distance: This column contains additional distance covered during specific activities that the user manually logged into the app. For example, if the user went for a run and entered the distance manually, it would be recorded here.

7. Very_Active_Distance: This column indicates the distance covered during activities classified as "very active," such as running, intense cardio, or high-intensity interval training.

8. Moderately_Active_Distance: This column represents the distance covered during activities classified as "moderately active," which may include brisk walking, cycling, or light jogging.

9. Light_Active_Distance: This column indicates the distance covered during activities classified as "light activity," such as casual walking, household chores, or light stretching.

10. Sedentary_Active_Distance: This column represents the distance covered while engaged in sedentary activities, such as sitting or lying down. It could be used to track inactive periods.

11. Very_Active_Minutes: This column records the number of minutes the user spent engaging in activities classified as "very active," typically high-intensity exercises that significantly elevate heart rate.

12. Fairly_Active_Minutes: This column contains the number of minutes spent engaging in activities classified as "fairly active," which are moderately intense activities that raise heart rate but are not as vigorous as "very active" activities.

13. Lightly_Active_Minutes: This column indicates the number of minutes spent engaging in activities classified as "lightly active," which include low-intensity activities that contribute to overall movement but do not significantly elevate heart rate.

14. Sedentary_Minutes: This column records the amount of time the user spent in sedentary behavior, such as sitting or lying down, without engaging in physical activity.

15. Calories_Burned: This column represents an estimate of the number of calories the user burned throughout the day based on their activity levels and other factors like age, weight, and gender. It's often calculated using algorithms that take into account activity data and user profile information.

Historical data back to 2002/03 for apprenticeship starts by age, level and sector subject area.

Gym User Dropout Prediction Dataset

Description

This synthetic dataset simulates user behavior in a fitness application, designed to predict the risk of gym membership dropout based on attendance patterns and personal attributes. The dataset contains 10,000 realistic user profiles with features that influence gym retention, making it ideal for classification tasks in behavioral analytics.

Key Features

• Realistic distributions matching actual gym user behavior patterns
• Complex feature interactions that simulate real-world decision-making
• Controlled noise to mimic natural data variability
• Balanced classes for effective machine learning modeling

Potential Use Cases

• Predicting at-risk users for retention interventions
• Analyzing factors contributing to gym commitment
• Developing personalized workout recommendations
• Behavioral segmentation of fitness app users

Dataset Characteristics

• Number of instances: 10,000
• Number of features: 8 predictive + 1 target
• Missing values: No
• Synthetic but realistic: Yes

Columns Description

Generation Methodology

Data was programmatically generated with: 1. Base distributions matching real gym statistics 2. Logical correlations between features (e.g., more sessions → longer durations) 3. Non-linear relationships in target variable 4. Controlled noise injection (Gaussian + categorical variability)

Suggested Evaluation Metrics

For classification models: - Precision-Recall curves (class imbalance consideration) - F1 score - ROC AUC - Feature importance analysis

License

CC0: Public Domain (Free to use for any purpose)

Acknowledgements

Synthetic dataset created for machine learning education and benchmarking purposes. Inspired by real fitness app analytics challenges.

Dataset Link

gym_user_dropout_dataset.csv

This dataset is a compilation of Statistics Canada Dissemination Areas with associated population data from the 2021 Census. Population is rounded to the nearest 5. There were several DA where the data was suppressed therefore the values are NULL.Statistics Canada 2021 Census Dissemination Area Boundary File, lda_000b21f_e.zip. Statistics Canada. Table 98-10-0023-01 Age (in single years), average age and median age and gender: Canada, provinces and territories, census divisions, census subdivisions and dissemination areas.Metadata

These data and associated R analysis file are associated with the paper: "Field Evidence of the Effects of Pro-sociality and Transparency on COVID-19 App Attractiveness" We ran 14 separate Google display ad campaigns from February 1 to 26. These were the only Google Display ads run for CovidDefense. Each campaign was targeted at people who reside in Louisiana via IP address. All campaigns used the same settings, ad destination, and ad image from the state of Louisiana's CovidDefense marketing materials. The 14 ads varied only in their text data in alignment with the 14 conditions summarized in this file (ads.csv). There are two primary datasets: one (data_demo.csv) which has all 7,010,271 impressions and demographic data, and another (data_geo.csv) with just the impressions that have associated geographic information. The former includes columns for Google-estimated demographics like Age and Gender, with many impressions having values of ``Unknown''. These two data tables for demographic and geographic impressions were represented by a row for each impression with columns for whether that impression resulted in a click; the age and gender or geography of the impression; as well as indicator variables for the presence or absence of ad information (appeals, privacy transparency -- broad privacy reassurance, non-technical control, and technical control -- and data transparency). An associated R file is included which includes functions to reproduce each model and associated statistics.

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).

Statistics Canada Census Data from 2021. This dataset includes the type age, sex and population data provided by Statistics Canada joined with the census tracts. Each topic covered by the census was exported as a separate table. Each table contains the total, male, and female characteristics as fields for each census tract. Topics range from population, age and sex, immigration, language, family and households, income, education, and labour. For more information on definitions of terms used in the tables and other notes, refer to Statistics Canada's 2021 Census.

As of January 2025, ** percent of social media users in the United States aged 40 to 49 years were users of Facebook, as were ** percent of ** to ** year olds in the country. Overall, ** percent of those aged 18 to 29 years were using Instagram in the U.S. The social media market in the United States The number of social media users in the United States has shown continuous growth in the past years, and it is forecast to continue increasing to reach *** million users in 2029. As of 2023, the social network user penetration in the United States amounted to an impressive ***** percent, meaning that more than nine in ten people in the country engaged with online platforms. Furthermore, Facebook was by far the most popular social media platform in the United States, accounting for ** percent of all social media visits in 2023, followed by Pinterest with **** percent of visits. The global social media landscape As of April 2024, **** billion people were social media users, accounting for **** percent of the world’s population. Northern Europe was the region with the highest social media penetration rate with a reach of **** percent, followed by Western Europe with **** percent and Eastern Asia **** percent. In contrast, less than one in ten people in Middle Africa used social networks. Facebook’s popularity is not limited to the United States: this network leads the market on a global scale, and it accumulated more than three billion monthly active users (MAU) as of 2024, which is far more any other social media platform. YouTube, Instagram, and WhatsApp followed, all with *** billion or more MAU.

Mobile devices especially smartphones have gained high popularity and become a part of daily life in recent years. Smartphones have built-in motion sensors such as accelerometer, gyroscope and orientation sensors. Recent researches on smartphones show that behavioral biometrics can be obtained from the smartphone motion sensors. In this context, we develop an Android application that collects accelerometer sensor data while user playing a game. This application records all accelerometer data and touch event information while users touch the screen. We perform two experiments and collect two different data using this application. In the first experiment, we collect data from 107 child users whose age vary from 4 to 11, and 100 adult users whose age are between 16 and 55. This dataset includes more than 11.000 taps data for child and adult users, in total. In the second experiment, data is collected from 60 female and 60 male users aged 17-57 for different activities like sitting and walking. There are more than 6.000 taps data for sitting and walking scenarios separately in the second dataset. We use popular Android smartphones in the experiments and they have all 100 Hz sampling rate. These data can be used for behavioral biometric analyses such as user age group and gender detection, user identification and authentication or tap event detection

Shows the age and sex profile information for dissemination areas was extracted from the Statistics Canada 2021 Beyond 20/20 browser software. It contains the information gathered during the 2021 Census with respect to the population within a dissemination area and the breakdown of this population by age and sex. This data covers the dissemination areas in York Region only. Statistics Canada has suppressed the profiles for four dissemination areas (35190040, 35190039, 35191328 and 35191046) due to very low population count and so will appear as NULL values in the attribute table.Please exercise caution if using dissemination areas to roll up (aggregate) to other levels of census geographies, due to greater suppression applied by Statistics Canada at dissemination area. Interested in viewing and interacting with this data even more? Visit the York Region Census Explorer Dashboard to gain high level insights from this data at the municipal and regional level for York Region.