Description:

• The dataset comprises anonymized data on apps available on the Google Play Store, capturing various aspects such as ratings, downloads, and categorization.
• The dataset has 10,841 entries, with some columns containing missing values, particularly in "Rating," "Type," "Content Rating," "Current Ver," and "Android Ver".
• This dataset can be utilized for analyzing trends in mobile app usage, user preferences, and app performance metrics across different categories.
• It can aid in understanding the impact of factors like app size, rating, and category on user downloads and popularity.
• This dataset can be utilized for analyzing app popularity, user preferences, and the relationship between app features (e.g., size, price) and downloads.
• It can help in identifying trends in app categories, assessing user satisfaction through ratings and reviews, and providing insights for app developers and marketers on user engagement and app performance.

Features:

🚀 MobileViews: A Large-Scale Mobile GUI Dataset

MobileViews is a large-scale dataset designed to support research on mobile agents and mobile user interface (UI) analysis. The first release, MobileViews-600K, includes over 600,000 mobile UI screenshot-view hierarchy (VH) pairs collected from over 20,000 apps on the Google Play Store. This dataset is based on the DroidBot, which we have optimized for large-scale data collection, capturing more comprehensive interaction details while… See the full description on the dataset page: https://huggingface.co/datasets/mllmTeam/MobileViews.

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.

This dataset simulates anonymized mobile screen time and app usage data collected from Android/iOS users over a 3-month period (Jan–April 2024). It captures daily usage trends across various app categories including:

Productivity: Google Docs, Notion, Slack

Entertainment: YouTube, Netflix, TikTok

Social Media: Instagram, WhatsApp, Facebook

Utilities: Chrome, Gmail, Maps

For YouTube, additional engagement statistics such as views, likes, and comments are included to analyze video popularity and content consumption behavior.

The dataset enables exploration of:

Productivity vs. entertainment screen time patterns

Daily usage fluctuations

App-specific user engagement

Correlation between time spent and user interactions

YouTube content virality metrics

This is a great resource for:

EDA projects

Behavioral clustering

Dashboard development

Time series and anomaly detection

Building recommendation or focus-assistive apps

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.

This is a dataset of the paper titled "Strategies to Embed Human Values in Mobile Apps: What do End-Users and Practitioners Think?". In this study, we conducted a mixed-methods empirical study, which collected data through 13 semi-structured interviews with Bangladeshi agriculture mobile app practitioners and 4 focus groups with 20 Bangladeshi female farmers. Our aim is to identify the extent to which the existing agriculture mobile apps reflect Bangladeshi female farmers' values and to propose potential strategies to address their values in agriculture apps. There are five documents in this dataset. Questionnaire for focus groups Questionnaire for interviews Examples of open coding process Summary of member checking outcomes Illustrative quotations for values and strategies

Explore Crawlora's mobile app dataset: 5,845,179 apps across both stores — 2,506,803 on Apple's App Store and 3,338,376 on Google Play. Categories, ratings, install scale, pricing and global availability — with REST API access.

Aggregate metrics over 5,768,976 apps across the Apple App Store (2,432,384) and Google Play (3,336,592): category mix, ratings, pricing, attention concentration, and global storefront availability — queryable via REST API.

These data have been collected and supplied by Huq Ltd. and comprise of records for the period July 2016 to October 2020. The data contain aggregated geolocated activity counts derived from mobile phone app use across Great Britain.

Mobile phone applications seek user’s consent for recording and storing the mobile device’s location when the app is in use. Activity counts are derived from these locations as the sum of distinct devices per grid cell per day. These data can be used as proxy for estimating activity levels and footfall across the UK.

These aggregate data were created from record level data which comprised individual phone IDs, and multiple entries for each mobile device if it is used multiple times for one app or the user accesses multiple apps. Thus, the following data cleaning and aggregation process has been used to create the activity counts:

1. Cleaning: Daily records comprise unique device ID, time-stamp and location of each entry collected by any app. The time-stamp is reformatted as a single daily date attribute.

2. Spatial linkage to OSGB grid: After turning the daily data-frames into spatial objects, the files are joined to the 1km x 1km OSGB grid, and each impression is attributed a grid cell ID corresponding to its latitude and longitude.

3. Creation of activity counts: Activity counts are created following the previous steps by counting the number of unique device IDs per grid cell per date. This removes multiple appearances of the same device (one device may collect multiple impressions through different apps or due to frequent usage). The final activity count corresponds to the number of unique devices within a 1km square for that day.

4. Output: The output comprises cleaned aggregation counts for each grid cell and day

N.B. More detail on how the data was collected and coverage is available if requesting for this detail in your initial application purpose, or if contacting us by email once you have made your initial application and received the form. Applicants would need to sign a non-disclosure agreement before accessing this detail, and such as request will significantly increase the time for data delivery. You can, of course, make a full application for the data without first receiving this collection/ coverage metadata.

Content

These data are provided at 1km x 1km OSGB Grid cells.

Activity counts of 1-10 devices are masked and replaced by “*” in the database, as low counts present potentially identifiable information.

For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page.

Quality, Representation and Bias

Excellent quality and coverage for major towns and cities. The data may be less complete for smaller settlements or more rural areas. Data are subject to suppression of potentially disclosive low counts as detailed above. Huq collects data from a varying mix of apps, the identities of which are commercially sensitive. Apps may be added to or deleted from the secure and summary data products over time. This, along with increasing national coverage and mobile phone uptake, results in general increases in apparent activity over the period covered by the data.

The dataset would benefit from comparison with population estimates (e.g. census data) to investigate coverage issues. 2016 data have the highest percentage of suppressed counts, and data suppression generally decreases over time, particularly in metropolitan (Met) areas. Data suppression levels in metropolitan areas generally fall below 50% by 2020.

This repository is part of the ITC-NetMingledApp dataset, which includes network traffic data from 36 Android applications, with each capture featuring concurrent traffic from multiple applications and smartphones. This repository contains part #1 of the data related to the Iran-Tehran scenario. Each capture is stored in a compressed file containing the relevant PCAP files of the associated applications. The PCAP files are named according to a convention: {TimeStamp}_{Application Name}{Download-Upload Speed}.pcap Part #2 of Iran-Tehran scenario is in the Tehran Dataset #2 (https://doi.org/10.17632/zsffy3j9y6.1) repository.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

App store data from Apple App Store and Google Play — rankings, ratings, reviews, categories and metadata. Mobile app and ASO intelligence, ready or custom.

📱 Mobile App Usage & Screen Time (2022–2024)

This synthetic dataset captures global mobile app usage behavior and screen time patterns across 2022 to 2024. It contains 10,000 usage records across 15 countries covering 80+ popular apps across 12 categories including Social Media, Gaming, Streaming, Productivity, Health & Fitness, Education, Shopping, Finance, and more.

📊 Dataset Highlights

• 10,000 records × 34 features — zero null values
• 80+ popular apps across 12 categories
• 15 countries | Android & iOS | 10 device brands
• Covers usage, monetization, notifications & digital wellbeing

🎯 Key Research Areas

• ⏱️ Screen time analysis by age group, gender & country
• 📱 App category engagement and session duration patterns
• 🔔 Notification overload impact on usage behavior
• 💰 In-app purchase prediction using ML classification
• 😴 Sleep disruption correlation with late-night phone use
• 🧠 Mental health impact of social media & gaming
• 🌿 Digital wellbeing feature adoption rates
• 📈 Screen time growth trend from 2022 to 2024

📋 Feature Categories

⚠️ Disclaimer

Fully synthetic dataset. No real user data was collected. For educational and research purposes only.

Author: Hamna Munir | License: CC0 Public Domain | Year: 2026

A mobile applications images and logos dataset typically consists of a collection of images representing the logos or user interfaces of various mobile apps. These datasets are commonly used for tasks such as logo recognition, mobile app classification, brand detection, and user interface analysis in the fields of computer vision and machine learning. Dataset Components: Images of Logos: This includes a wide variety of app logos, ranging from popular apps to lesser-known ones. Logos can be in different formats (e.g., PNG, JPG) and may vary in size and resolution. The dataset might contain both full-color logos and monochrome variations.

App Icons: Apart from the logo, the dataset may contain app icons as they appear on mobile devices' home screens. These icons are typically square or rounded in shape, with resolutions like 512x512 or 1024x1024 pixels.

User Interface (UI) Screenshots: Some datasets also include screenshots of the mobile app’s interface, capturing various screens like the home screen, settings, or functional pages. This component is useful for UI/UX analysis, app design comparison, or screen element recognition.

Class Labels: Each image in the dataset is usually associated with metadata or labels. These labels may include:

App Name: The name of the mobile application. Category: The app’s category (e.g., Social Media, Finance, Gaming, etc.). Brand Name: The brand associated with the app (e.g., Facebook, Twitter). Platform: The operating system for which the app was developed (iOS, Android). Resolution: The size or pixel dimensions of the image. Image Annotations: Some datasets may provide additional annotations, such as bounding boxes around logos or key design elements. These annotations are essential for object detection or logo localization tasks.

Typical Uses: Logo Recognition: Mobile app logos are used in machine learning algorithms to recognize brands or products automatically from images or videos.

App Classification: The dataset can help classify images of app interfaces into predefined categories, such as social media, gaming, or finance.

Brand Analysis: The dataset allows researchers to study how logos evolve over time, or how brand identity is reflected in app icons.

User Interface (UI) Research: UI screenshots can be analyzed to understand app design patterns, common layouts, or usability across different categories of mobile applications.

Collection Process: Web Scraping: App logos, icons, and UI screenshots are often collected from app stores (Google Play, Apple App Store) through web scraping techniques.

Manual Curation: In some cases, datasets are manually curated, where researchers download app images and organize them according to specific categories or criteria.

MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments

Mobile World is a substantially more challenging mobile-use benchmark designed to better reflect real-world mobile usage. It comprises 201 tasks across 20 applications, featuring long-horizon, cross-app tasks, and novel task categories including agent-user interaction and MCP-augmented tasks. The difficulty of Mobile World is twofold:

Long-horizon, cross-application tasks.… See the full description on the dataset page: https://huggingface.co/datasets/Tongyi-MAI/MobileWorld.

This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.

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.

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.

PARROT2025_mitmproxy Dataset: Mobile App Traffic Traces with SSL Keys

Dataset Overview:

• Traffic from 80 Android applications selected from the MAppGraph dataset app list
• 4 traffic traces per application
• Average duration of 5 minutes per trace
• Total of 320 traffic captures spanning more than 25 hours of app traffic

Traffic Characteristics:

Each trace captures the network traffic generated during the first deployment of the application following installation. The traffic represents app initialization behavior and startup communications patterns.

Capture System Configuration:

• Traffic captured using the PARROT capture system with mitmproxy traffic interception
• Android SDK API level 35 (Android 15) emulator
• DNS configuration: dns.google (8.8.8.8)
• Capture location: Universidad Carlos III de Madrid, Spain
• Capture year: 2025
• Traffic generation: Manual interaction with the emulator by research team members

Data Format and Organization:

The dataset is contained in the folder "dataset_PARROT2025_mitmproxy" which includes:

• Raw network traffic traces in PCAP format
• Corresponding SSL session keys for traffic decryption

Research Applications:

This dataset enables research in mobile app traffic analysis, network security, protocol evolution studies, and encrypted traffic analysis. The inclusion of SSL keys allows researchers to analyze both encrypted and decrypted communications patterns.

Global app downloads have plateaued in recent years, especially when comparing between the previous figures provided by data.ai and Sensor Tower. However, global downloads seemed to have recovered in 2025, reaching nearly *** billion unique downloads. Why the difference? Source methodology explains the gap The discrepancy arises from considerable differences in the methodology used by the sources to aggregate and generate the data. Sensor Tower reports only unique downloads per user account, excluding app updates, re-downloads, and installations on multiple devices by the same user. In contrast, data.ai includes these additional activities as well as downloads from third-party Android stores and a broader geographic scope, resulting in substantially higher total counts. As a result, Sensor Tower's numbers better reflect new user acquisition, while data.ai's encompass all market activity and total engagement. Despite stagnating downloads, user spending is growing While the number of downloads is leveling off, consumer spending on in-app purchases and related revenue has grown in 2025 to *** billion U.S. dollars, up from around *** billion U.S. dollars in 2023. While gaming remains the highest-grossing app category overall, other categories drove the growth. The entertainment, photo & video, productivity, and social networking categories each grew by at least *** billion U.S. dollars in revenue in 2025 compared to the previous year.