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.

Monitoring statistics for a real world mobile app

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.

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

Dataset Card for [Dataset Name]

  Dataset Summary

It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches)

  Supported… See the full description on the dataset page: https://huggingface.co/datasets/app_reviews.

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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 apks, 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}
}

Dataset

This dataset was created by Wael Shaher

Released under CC0: Public Domain

Contents

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.

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)

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.

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.

Analysys Qianfan is the data service affiliated with the widely-known third-party digital economy industry research house Analysys. Through a proprietary SDK embedded in more than 30k partner mobile apps, as well as external data provided by the three major wireless carriers in China, Analysys Qianfan is able to monitor online activity for 600m+ monthly active devices, or 86.9m DAU, as of November 2020. The firm identifies ~300 behavioural metrics for mobile internet users and provides this for 30k+ mobile apps across dozens of sub-sectors, including healthcare, food & gourmet, ride sharing, SaaS and financial services. Examples of available metrics include: app name; number of average active users per hour, day, week and month; average time spent on app; penetration rate; retention rate; user age; gender; phone model. Marketers, venture capitalists, and equity investors are using Analysys QF’s data to gauge user penetration and engagement for products and campaigns, identify investable start-ups, and monitor the popularity of mobile app products and services for listed companies. Data is presented in Chinese, Japanese and English and accessible via email, website or Weixin mini program. Annual pricing fees are dependent on the required universe coverage but typically range between 100k-1m RMB per annum.

This was taken from The Android App Market on Google Play project on DataCamp.

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 extracted features from Hybrid Apps available for deployment on the Android platform until recently. The data for this dataset has been culled out from various sources, including existing similar datasets and Google Play Store or its mirrors. The dataset is labelled to differentiate malicious and benign Hybrid Apps. Thus, it may conveniently be used for supervised learning. Nonetheless, the dataset has adequate attributes to support any unsupervised learning task as well. The dataset comprises 78,767 samples.

As of May 2023, the mobile app of shopping and marketplace platform Etsy used approximately half of its collected data points to track users. In comparison, health app Noom used only one of its collected user data point for tracking purposes.

Network traffic analysis, i.e. the umbrella of procedures for distilling information from network traffic, represents the enabler for highly-valuable profiling information, other than being the workhorse for several key network management tasks. While it is currently being revolutionized in its nature by the rising share of traffic generated by mobile and hand-held devices, existing design solutions are mainly evaluated on private traffic traces, and only a few public datasets are available, thus clearly limiting repeatability and further advances on the topic. To this end, we have designed and implemented MIRAGE, a reproducible architecture for mobile-app traffic capture and ground-truth creation. The outcome of this system is MIRAGE-2019, a human-generated dataset for mobile traffic analysis (with associated ground-truth) having the goal of advancing the state-of-the-art in mobile app traffic analysis. MIRAGE-2019 is expected to be capitalized by the networking community for different tasks related to mobile traffic analysis.

As of May 2023, Facebook collected the larger number of total unique data points from global iOS users, around 32 data points. Popular digital payment app PayPal and Airbnb collected 26 data points each, while AI tool photo and image editing apps Photoleap collected around 14 unique data points.

The Collective is the largest independent global data marketplace (DMP) with over ten billion all-time MAIDs and over 68M MAU and 8M DAU in our App Activity data feed.

We also take great pride in the curation of our supplier network which is comprised of over 50 top tier, privacy first, and fully consented aggregators and publishers to ensure compliance, quality and scale.

This listing focuses on app usage data, SDK Data, and App Session Data. Do not hesitate to contact Collective Data Solutions and access to the best Mobile App Data on the market.