This dataset provides comprehensive insights into mobile app usage patterns across different categories, including education, social media, productivity, entertainment, health, news, and shopping applications. It contains screen time data for 500 users with demographic information such as age and gender, making it valuable for analyzing digital behavior patterns and productivity correlations.

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.

🚀 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.

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.

Aggregate metrics over 5,063,929 apps across the Apple App Store (2,113,447) and Google Play (2,950,482): category mix, ratings, pricing, attention concentration, and global storefront availability — queryable via REST API.

The MASC dataset is the foundation for developing machine-learning models to detect and classify these screen types. Some of advantages of the MASC dataset, composed of mobile application screens that I have collected, can be summarized as follows: • Large and Diverse App Screen Sample: The MASC dataset includes over 7,000 unique mobile app screens from various apps and activity types, so it can support the development of robust ML models for mobile app screen classification. and it can also serve as a benchmark for developing and evaluating new ML models in this domain. • Realistic Data: Screens collected from actual Android apps via the Rico platform represent real-world designs, aiding models' generalization to real apps. • Improved App Accessibility: Identifying common screen patterns can offer insights to enhance accessibility features, benefiting users with disabilities. • Enhanced User Experience: Understanding mobile app screen types can lead to better user interface design, improving the overall user experience. • many potential applications can be created using the MASC Dataset. These applications include UI captioning and semantic tagging, user-friendly designs with explanations, intelligent tutorials, and enhanced design search features.

Explore Crawlora's mobile app dataset: 4,348,551 apps across both stores — 1,409,323 on Apple's App Store and 2,939,228 on Google Play. Categories, ratings, install scale, pricing and global availability — with REST API access.

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.

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.

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.

ChatGPT is no longer a web-first product. Its mobile app crossed 1.1 billion monthly active users in April 2026 and has been downloaded more than 1.9 billion times across iOS and Android since launching in May 2023 — making it one of the most-installed consumer apps of the decade and the only AI product anywhere near that scale. This page tracks how mobile adoption grew, how the install curve has cooled from its 2025 peak even as engagement and revenue climb, how consumer spending inside the app has compounded, and how adoption splits between Apple's App Store and Google Play.

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.

This dataset provides information about 837 mobile applications (apps) published across the whole world to fight the COVID-19 crisis. This information includes: (a) information available in the mobile app stores (Apple App Store and Google Play) between 20/04/2020 and 02/08/2020; (b) complementary information obtained from manual analysis performed until mid-September 2020; and (c) status information about app availability on 28/02/2021, when we last visited the mobile app stores. The dataset is one of the outcomes of the JRC Unit B.6 multi-channel approach to the monitoring and analysis of COVID-19-related mobile apps.

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.

Real Mobile Apps Parsed from Apple AppStore for the Market in 2026.

This dataset contains cleaned data from the Google Play Store, which includes information on various mobile applications available on the platform. The original dataset was sourced from [mention the source if available, e.g., Kaggle, official API, etc.], and it has been preprocessed to remove inconsistencies, handle missing values, and standardize formats for easier analysis.

Features: The dataset includes the following columns: - App Name: The name of the mobile application. - Category: The category under which the app is listed on the Play Store. - Rating: The user rating of the app (0-5 scale). - Reviews: The number of user reviews. - Size: The size of the app (e.g., in MB). - Installs: The number of times the app has been installed. - Type: Whether the app is free or paid. - Price: The price of the app, if paid. - Content Rating: The age group suitable for the app. - Genres: The genres associated with the app. - Last Updated: The last date the app was updated. - Current Version: The current version of the app. - Android Version: The minimum Android OS version required to run the app.

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 four documents in this dataset.

1. Questionnaire for focus groups
2. Questionnaire for interviews
3. Examples of open coding process
4. Summary of member checking outcomes

As of January 2025, StartApp dominated among Android apps that used mobile data intelligence software development kit (SDK) with nearly 51 percent integration reach. Second-ranked Comscore Analytics was integrated with over 12 percent of apps that used data intelligence SDKs.

This dataset encompasses mobile smartphone application (app) usage, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or surveying to understand the why. iOS and Android operating system coverage.

Quantify mobile app popularity and user acquisition by tracking downloads across major app stores, offering insights into market penetration and growth.