As of October 2020, the average amount of mobile data used by Apple Maps per 20 minutes was 1.83 MB, while Google maps used only 0.73 MB. Waze, which is also owned by Google, used the least amount at 0.23 MB per 20 minutes.

As of January 2022, the money app Greenlight had the highest number of data segments tracked, collecting 22 different types of data from its users. Launched in 2017, Greenlight is a fintech app for children that allows parents to manage and monitor allowances and spending. Mobile gaming app Pokémon GO was the second most invasive mobile app used by children, collecting 17 different data segments from its users. Only the Amazon Kids+ app and the Kinzoo Social app appeared to collect data over sensitive information from their users.

As of February 2025, video apps accounted for around 76 percent of global mobile data usage every month. Second-ranked social networking accounted for eight percent of global mobile data volume. The two categories, though, can easily overlap, as users can watch videos via video applications, as well as on social networking applications. Most popular social media platforms with video content Facebook, YouTube, and Instagram were among the most popular social networks in the world, as of October 2021. Each of these platforms allow to post, share, and watch video content on a mobile device. One of the fastest growing global brands, Tiktok, is also a social media platform where users can share video content. In September 2021, the platform reached 1 billion monthly active users. Leading types of mobile video content in the U.S. The United States was the third country in the world based on the number of smartphone users as of May 2021, with around 270 million users. Therefore, mobile content usage in the country was one of the highest in the world, and a big part of it was video content. As of the third quarter of 2021, more than 80 percent of survey respondents in the United States reported watching YouTube on their mobile devices. Social media videos were the second most popular type of content for mobile audiences, with almost six in 10 respondents watching videos on social media platforms like TikTok and Twitter.

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

******************************************* are the top two answers among Indian consumers in our survey on the subject of "Most used smartphone apps by type".Find this and more survey data on most used smartphone apps by type in our Consumer Insights tool. Filter by countless demographics, drill down to your own, hand-tailored target audience, and compare results across countries worldwide.

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

User Feedback Dataset from the Top 15 Downloaded Mobile Applications

This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. The dataset is particularly suitable for Natural Language Processing (NLP) applications, such as text classification and topic modeling.

List of Included Applications:

• TikTok
• Instagram
• Facebook
• WhatsApp
• Telegram
• Zoom
• Snapchat
• Facebook Messenger
• Capcut
• Spotify
• YouTube
• HBO Max
• Cash App
• Subway Surfers
• Roblox

Data Columns and Descriptions:

• review_id: Unique identifiers for each user feedback/application review.
• content: User-generated feedback/review in text format.
• score: Rating or star given by the user.
• TU_count: Number of likes/thumbs up (TU) received for the review.
• app_id: Unique identifier for each application.
• app_name: Name of the application.
• RC_ver: Version of the app when the review was created (RC).

Terms of Use:

This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference:

1.Paper

M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3332644

2.Dataset

Asnawi, M. H., Pravitasari, A. A., Herawan, T., & hendrawati, T. (2023). User Feedback Dataset from the Top 15 Downloaded Mobile Applications [Data set]. In The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling (1.0.0, Vol. 11, pp. 130272–130286). Zenodo. https://doi.org/10.5281/zenodo.10204232

Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.

Key Health App StatisticsTop Health AppsHealth & Fitness App Market LandscapeHealth App RevenueHealth Revenue by AppHealth App UsageHealth App Market ShareHealth App DownloadsKeeping track of...

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

Key US App Market StatisticsUS App Market SizeUS App Market Revenue by AppUS Smartphone UsersUS Smartphone PopulationTime Spent on Apps in the USUS App Market DownloadsUS Downloads by AppUS Daily...

Mobile apps are everywhere. They are easy to create and can be very lucrative from the business standpoint. Specifically, Android is expanding as an operating system and has captured more than 74% of the total market[1].

The Google Play Store apps data has enormous potential to facilitate data-driven decisions and insights for businesses. In this notebook, we will analyze the Android app market by comparing ~10k apps in Google Play across different categories. We will also use the user reviews to draw a qualitative comparision between the apps.

The dataset you will use here was scraped from Google Play Store in September 2018 and was published on Kaggle. Here are the details:

googleplaystore.csv This file contains all the details of the apps on Google Play. There are 9 features that describe a given app. App: Name of the app Category: Category of the app. Some examples are: ART_AND_DESIGN, FINANCE, COMICS, BEAUTY etc. Rating: The current average rating (out of 5) of the app on Google Play Reviews: Number of user reviews given on the app Size: Size of the app in MB (megabytes) Installs: Number of times the app was downloaded from Google Play Type: Whether the app is paid or free Price: Price of the app in US$ Last Updated: Date on which the app was last updated on Google Play

googleplaystoreuserreviews.csv This file contains a random sample of 100 most helpful first user reviews for each app. The text in each review has been pre-processed and passed through a sentiment analyzer. App: Name of the app on which the user review was provided. Matches the App column of the apps.csv file Review: The pre-processed user review text Sentiment Category: Sentiment category of the user review - Positive, Negative or Neutral Sentiment Score: Sentiment score of the user review. It lies between [-1,1]. A higher score denotes a more positive sentiment. From here on, it will be your task to explore and manipulate the data until you are able to answer the three questions described in the instructions.

The goal of this study is to measure willingness to participate in passive mobile data collection among German smartphone owners. The data come from a two-wave web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2016, 2,623 participants completed the Wave 1 questionnaire on smartphone use and skills, privacy and security concerns, and general attitudes towards survey research and research institutions. In January 2017, all respondents from Wave 1 were invited to participate in a second web survey which included vignettes that varied the levels of several dimensions of a hypothetical study using passive mobile data collection, and respondents were asked to rate their willingness to participate in such a study. A total of 1,957 respondents completed the Wave 2 questionnaire.

Wave 1

Topics: Ownership of smartphone, mobile phone, PC, tablet, and/or e-book reader; type of smartphone; frequency of smartphone use; smartphone activities (browsing, e-mails, taking photos, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, play games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, statistical office, mobile service provider, app companies, credit card companies, online retailer, and social networks); concerns regarding the disclosure of personal data by the aforementioned institutions; general privacy concern; privacy violated by banks/ credit card companies, tax authorities, government agencies, market research companies, social networks, apps, internet browsers); concern regarding data security with smartphone activities for research (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth); number of online surveys in which the respondent has participated in the last 30 days; Panel memberships other than that of mingle; previous participation in a study with downloading a research app to the smartphone (passive mobile data collection).

Wave 2

Topics: Willingness to participate in passive mobile data collection (using eight vignettes with different scenarios that varied the levels of several dimensions of a hypothetical study using passive mobile data collection. The research app collects the following data for research purposes: technical characteristics of the smartphone (e.g. phone brand, screen size), the currently used telephone network (e.g. signal strength), the current location (every 5 minutes), which apps are used and which websites are visited, number of incoming and outgoing calls and SMS messages on the smartphone); reason why the respondent wouldn´t (respectively would) participate in the research study used in the first scenario (open answer); recognition of differences between the eight scenarios; kind of recognized difference (open answer); remembered data the research app collects (recall); previous invitation for research app download; research app download.

Demography: sex; age; federal state; highest level of school education; highest level of vocational qualification.

Additionally coded was: running number; respondent ID; duration (response time in seconds); device type used to fill out the questionnaire; vignette text; vignette intro time; vignette time.

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Each app (row) has values for catergory, rating, size, and more.

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

googleplaystore.csv

googleplaystore_user_reviews.csv

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Dataset

This dataset was created by SHYAM GUPTA

Released under CC0: Public Domain

Contents

Key Mobile Payments StatisticsTop Mobile Payments AppsFinance App Market LandscapeMobile Payments Transaction VolumeMobile Payments UsersMobile Payments Adoption by CountryMobile Payments TPV in...

As of May 2023, the mobile app version of popular ********************************* used ** of the data points they collected to track their iOS users, as well as collecting ** data points connected to the user's identity. Facebook, which was identified as the most data-hungry app among all the mobile social media, used ***** of its ** collected data points to track users. Dating app ****** collected ** data points collected to the users' identity, as well as **** data points to track users activity.

Link to code

Description:

The **company* that I work for builds iOS & Android mobile applications that are available in the App Store (iOS) and on Google Play (Android). I am a 'data analyst' at this company and am responsible for guiding the software developers in making data-driven decisions in regards to which apps they should build.

**This project was completed as part of a DataQuest course and was not used for a real company.*

Plan:

The criteria that my company has laid out for a successful app can be determined as follows:

• Create a minimal Android version of the application and add it to Google Play.
• The app will be developed further IF it gets a good response from users.
• If app continues to be profitable after 6 months, an iOS version will be built and added to the App store.

The applications my company builds are all free for users to download and install. Our revenue mainly comes from in-app ads, so the number of users for any given app directly influences our profit.

Goal:

The main goal for this project is to analyze data and give our developers more insight on which kind of apps are more likely to attract users.

Conclusion:

Throughout this project, I analyzed data for the mobile apps in the App Store and Google Play in order to understand which apps would be profitable for both markets. I concluded that turning a popular book into an app could become profitable for both Google Play and the App Store. The team might include an audible version of the book, trivia, in-app platform to discuss with other users, daily quotes and more within the app.

The two .csv files for analysis: App Store Google PlayStore

Context

I had a dataset of about 202 million Android smartphone logs (from +14.000 users) at my disposal which we had to contextualize for academic research purposes. Since the database contained the name of the app as registered on the Android phone (e.g. com.nianticlabs.pokemongo), it was relatively easy to build a scraper to collect some additional info on the apps (e.g. genre of app, permissions of app, etc.). In total, I scraped metadata on more than 50.000 apps.

The difference between other available app datasets (on Kaggle) is that:

1. The scraper collected data from five different platforms, not just Google Play. This decreased the negative impact legacy versions and discarded apps had on the amount of missings in the final dataset. The scraper took on a sequential scraping strategy, meaning that it started its search on the Play Store and sequentially looked on other platforms if the app was not available on the Google platform. All app categories are harmonized with the Google Play categories functioning as the gold standard.

2. I performed an extensive automated and manual quality check of the data obtained from these repositories (see 'content' paragraph). Although some of these checks are relatively automated (e.g. fuzzymatching), the most laborious check involved ranking the apps by popularity among the users in the database and looking for inconsistencies. For example, both legitimate sport apps (e.g. Strava) and sport games (e.g. FIFA) are categorized in Google Play as 'sports'. For this reason, I created an additional 'sport games' category. Another example would be the creation of a separate dating-app category; as these apps are officially categorized as a "lifestyle" (or sometimes "social") app, which is not only inconsistent but above all vague. The new category column is the end result of this manual check.

Given both the sequential scraping strategy and the multiple data quality checks performed, this is probably one of the most valid and extensive Android app datasets out there.

Obviously, some variables were not available on some of the platforms. Here's a quick overview of the variables, including an indication of whether this specific parameter was available on the platform:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2F5ce86f6ab8bb43145df08255d76e5a3f%2Fapp%20scraper%20variables.PNG?generation=1597328745448228&alt=media" alt="">

Around 2000 apps were not found in any repository, but are still included in the dataset (indicated by the "not found in databases" string in multiple columns).

The .csv file is the original file format of the dataset, but since dealing with csv files is probably a major cause of anger fits among data analysts around the globe, I also included an Excel version of the file just in case.

If you would like to use this dataset for your own research, but you're afraid the reviewers will question the performed 'manual check', just cite one of these (or both) papers:

• The extended abstract on the development of the scraper, presented at the ICA 2020 conference

Boghe, K., De Grove, F., Herrewijn, L., & De Marez, L. (2020). Scraping application data from the web— Addressing the temporality of online repositories when working with trace data. Extended abstract presented at the 70th International Communication Association Conference

• A publication where we use the dataset presented here

Boghe, K., Herrewijn, L., De Grove, F., Van Gaeveren, K., & De Marez, L. (2020). Exploring the effect of in-game purchases on mobile game use with smartphone trace data. Media and Communication,8(3). doi: 10.17645/mac.v8i3.3007

Citing these two references will probably (and hopefully) serve as some kind of previous validation/'vetting' for your reviewers.

Content

Since I wrote an extended abstract based on my experience with writing the scraper, I'll just shamelessly copy/paste a couple of paragraphs from said abstract to provide some additional info here.

"One of the main objectives of our scraper was to deal with the inherent temporality of web data and app marketplaces. Not only do apps gradually disappear from depositories, but subtle name changes and the existence of legacy versions complicate matters further. While Google Play serves as the golden standard for Android applications, the information-value of this repository diminishes rapidly as the age of the historical data increases. For this reason, we go beyond Google’s Play Store and additionally use alternative repositories. Such repositories are often less well-maintained and thus contain information...

[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!