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.

Unlock valuable insights with the Google Play Store Android Apps Dataset in CSV format, featuring detailed information on over thousands of Android apps available on the Google Play Store. This comprehensive dataset includes key attributes such as App Name, App Logo, Category, Description, Average Rating, Ratings Count, In-app Purchases, Operating System, Company, Content Rating, Images, Email, Additional Information, and more.

Perfect for market researchers, data scientists, app developers, and analysts, this dataset allows for deep analysis of app performance, user preferences, and industry trends. With data on app descriptions, content ratings, in-app purchases, and company information, you can track trends in the mobile app market, evaluate user satisfaction, and conduct competitive analysis.

The dataset is ideal for businesses looking to optimize app strategies, enhance user experience, and improve app performance based on real user feedback. Easily import the data into your favorite analysis tools to gain actionable insights for your app development or research.

With regularly updated data scraped directly from the Google Play Store, the Google Play Store Android Apps Dataset is an invaluable resource for anyone looking to explore trends, track performance, or enhance their app strategies.

Dataset

This dataset was created by Wael Shaher

Released under CC0: Public Domain

Contents

We built a crawler to collect data from the Google Play store including the application's metadata and APK files. The manifest files were extracted from the APK files and then processed to extract the features. The data set is composed of 870,515 records/apps, and for each app we produced 48 features. The data set was used to built and test two bootstrap aggregating of multiple XGBoost machine learning classifiers. The dataset were collected between April 2017 and November 2018. We then checked the status of these applications on three different occasions; December 2018, February 2019, and May-June 2019.

As COVID-19 continues to spread across the world, a growing number of malicious campaigns are exploiting the pandemic. It is reported that COVID-19 is being used in a variety of online malicious activities, including Email scam, ransomware and malicious domains. As the number of the afflicted cases continue to surge, malicious campaigns that use coronavirus as a lure are increasing. Malicious developers take advantage of this opportunity to lure mobile users to download and install malicious apps.

However, besides a few media reports, the coronavirus-themed mobile malware has not been well studied. Our community lacks of the comprehensive understanding of the landscape of the coronavirus-themed mobile malware, and no accessible dataset could be used by our researchers to boost COVID-19 related cybersecurity studies.

We make efforts to create a daily growing COVID-19 related mobile app dataset. By the time of mid-November, we have curated a dataset of 4,322 COVID-19 themed apps, and 611 of them are considered to be malicious. The number is growing daily and our dataset will update weekly. For more details, please visit https://covid19apps.github.io

This dataset includes the following files:

(1) covid19apps.xlsx

In this file, we list all the COVID-19 themed apps information, including apk file hashes, released date, package name, AV-Rank, etc.

(2)covid19apps.zip

We put the COVID-19 themed apps Apk samples in zip files . In order to reduce the size of a single file, we divide the sample into multiple zip files for storage. And the APK file name after the file SHA256.

If your papers or articles use our dataset, please use the following bibtex reference to cite our paper: https://arxiv.org/abs/2005.14619

(Accepted to Empirical Software Engineering)

@misc{wang2021virus, title={Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware}, author={Liu Wang and Ren He and Haoyu Wang and Pengcheng Xia and Yuanchun Li and Lei Wu and Yajin Zhou and Xiapu Luo and Yulei Sui and Yao Guo and Guoai Xu}, year={2021}, eprint={2005.14619}, archivePrefix={arXiv}, primaryClass={cs.CR} }

This dataset consists of apps needed permissions during installation and run-time. We collect apps from three different sources google play, third-party apps and malware dataset. This file contains more than 5,00,000 Android apps. features extracted at the time of installation and execution. One file contains the name of the features and others contain .apk file corresponding to it extracted permissions and API calls. Benign apps are collected from Google's play store, hiapk, app china, Android, mumayi , gfan slideme, and pandaapp. These .apk files collected from the last three years continuously and contain 81 distinct malware families.

AndroR2 is a dataset of 90 manually reproduced bug reports for Android apps listed on Google Play and hosted on GitHub, systematically collected via an in-depth analysis of 459 reports extracted from the GitHub issue tracker. For each reproduced report, AndroR2 includes the original bug report, an apk file for the buggy version of the app, an executable reproduction script, and metadata regarding the quality of the reproduction steps associated with the original report. We believe that the AndroR2 dataset can be used to facilitate research in automatically analyzing, understanding, reproducing, localizing, and fixing bugs for mobile applications as well as other software maintenance activities more broadly.

Dataset Card for Dataset Name

  Dataset Summary

MobileRec is a large-scale app recommendation dataset. There are 19.3 million user\item interactions. This is a 5-core dataset. User\item interactions are sorted in ascending chronological order. There are 0.7 million users who have had at least five distinct interactions. There are 10173 apps in total.

  Supported Tasks and Leaderboards

Sequential Recommendation

  Languages

English… See the full description on the dataset page: https://huggingface.co/datasets/recmeapp/mobilerec.

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.

Context

To date (April 2020), Android is still the most popular mobile operating system in the world. Taking into account billion of Android users worldwide, mining this data has the potential to reveal user behaviors and trends in the whole global scope.

Content

There are 2 CSV files: - app.csv with 53,732 rows and 18 columns. - comment.csv with 1,468,173 rows and 4 columns.

The scraping was done in April 2020.

Acknowledgements

This dataset is obtained from scraping Google Play Store. Without Google and Android, this dataset wouldn’t have existed.

The dataset is first published in this blog.

Inspiration

Business trends on mobile can be explored by examining this dataset.

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

The following academic datasets provided the frequencies of prominent binary-features, namely Android application permission requests* for the negative and positive classes. They are used in analyzing the dataset dissimilarities via the method proposed by Gürol Canbek.

Negative/Positive dataset pairs** and their references - DS0 by (Lindorfer et al., 2014). (ANDRUBIS) as it is called "Touchstone Dataset" in the related article - DS1 by (Aswini and Vinod, 2014). (Contagio) - DS2 by (Wang et al., 2014)*** - DS3 by (Yerima et al., 2014)*** - DS4 by (Jiang and Zhou, 2013). (Android Malware Genome Project, AMGP) - DS5 (Peng et al., 2012)

Thanks to the authors providing the data.

• https://developer.android.com/reference/android/Manifest.permission.html ** DS4 is a positive-class only dataset (it does not negative-class samples) *** DS2 and DS3 positive-class datasets contain DS4 (AMGP) samples.

Aaditya1/Mobile-Application-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset contains 950 Android application logs from different malware categories. Applications are instrumented by human (real human-interaction) using DroidCat Logger tool so the behavior logs highly assemble real world executing of Android apps. The dataset contains 440 malicious and 508 benign (normal) app logs. The logs have been captured for XDroid project. You can find more details on the dataset in the paper.

This dataset was created using Wireshark. The dataset contains a total of 30 encrypted communication records, 3 records (.pcap) were created for each application. The records were obtained from a mobile device that was connected to the laptop using wifi technology. The laptop was connected to the Internet and contained a running instance of Wireshark to create a record. The telephone had been restarted before each record was created. After connecting to the network, the device was left without user interaction for 5 minutes. At the end of the 5-minute period, Wireshark recording was started. In the meantime, the interaction with the given application was taking place on the mobile device. The dataset was created to identify mobile applications in encrypted traffic based on TLS fingerprints.

This dataset has been collected from two different sources. The first dataset was taken from [1] and collected by Panichella et al. We obtained this dataset from Dr. Sebastiano Panichella via email. This dataset contains reviews of the AngryBirds, Dropbox, and Evernote app, which were taken from Apple’s App Store, other reviews were taken from Android’s Google Play store such as TripAdvisor, PicsArt, Pinterest and Whatsapp. This dataset consist of with 1390 reviews from all previously mentioned apps and all reviews were classified into four classes related to Software engineering’s maintenance task as follows: 192 reviews as Feature Request (FR), 494 reviews as Problem Discovery (PD), 603 reviews as Information Gaing (IG) and 101 reviews as Information Seeking (IS). We indicate to this dataset as “Pan Dataset”. The second dataset is used in [2] and prepared by Maalej et al. It is available at Hamburg University website on this direct link (https://mast.informatik.uni-hamburg.de/app-review-analysis). The truth dataset contains 3691 reviews from different Google’s apps store and Apple’s app store. We indicate to this dataset as “maalej dataset”. All reviews were classified into four classes related to Software engineering’s maintenance task as follows: 252 reviews as Feature Request (FR), 370 reviews as bug report/Problem Discovery (BR/PD), 607 reviews as User Experience (UE) and 2461 reviews as Rating (RT)

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.

As of March 2021, YouTube Kids and Facebook Messenger Kids were the mobile apps for children found to collect the largest amount of data from global iOS users. The apps collected a total of 15 data points from each of the examined data types,. Language learning app Lingokids and educational app ABCmouse followed with 10 data points. The type of data that the examined children's apps collected mostoften were contact information and diagnostics.

Children mobile privacy From online education to gaming and social media, children and young users are increasingly active in online environments via mobile devices. In 2021, playing online games and watching YouTube videos figured among the most popular mobile activities for kids worldwide, while less than five in 10 reported using their phones to complete assignments for school. As vulnerable users, children are entitled to institutional protection and lower interference from tech companies. However, mobile apps designed for children still collect data from their young users. As of the beginning of 2022, money management and gaming apps were the app categories found to track the largest number of data segments from children, with 10.1 and 9.3 data points tracked, respectively.

Child proof social media? While the impact of social media on younger users’ development is yet to be fully understood, parents and educators were quick to realize that social media expands the range of dangers children can encounter while being online. In 2021, children in the United States and in the United Kingdom spent an average of 98 minutes per day on TikTok, as well as 83 minutes daily on Snapchat. In the U.S., both Snapchat and TikTok agreed to respect the age limit restrictions set by the Children's Online Privacy Protection Act (COPPA), and while Snapchat discontinued its children-specific Snapkidz app in 2016, TikTok relies on its TikTok Younger Users platform for users younger than 13. Despite the majority of social media services requiring users to be at least 13 years old, a survey conducted in 2021 in the United Kingdom has found that 60 percent of all surveyed kids aged between eight and 11 had their own social media profile.

Dataset Description: Frappe Processed Dataset The Frappe dataset has been processed to refine the quality of user-item interactions by removing entries where either users or items had fewer than 5 interactions. This pruning resulted in a significant reduction in the dataset size:

Number of Users: 651 (a reduction of 31.97% from the original dataset) Number of Items: 1127 (a reduction of 72.39%) Total Number of Interactions: 84,373 (a reduction of 12.30%)

Columns Overview: The dataset… See the full description on the dataset page: https://huggingface.co/datasets/abadesalex/Frappe-mobile-app-usage.