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Mining User Privacy Concern Topics from App Reviews

https://doi.org/10.2139/ssrn.4785210
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Outgoing claims: the papers this work cites, builds on, or argues with.

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A requirements taxonomy for reducing Web site privacy vulnerabilities
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On the automatic classification of app reviews
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Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques
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Mining non-functional requirements from App store reviews
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FeatCompare: Feature comparison for competing mobile apps leveraging user reviews
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Revealing the unrevealed: Mining smartphone users privacy perception on app markets
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Managing privacy in the digital economy
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Static analysis of android apps: A systematic literature review
inferredcites →https://doi.org/10.1016/j.infsof.2018.12.002openalex-extracted
Software feature refinement prioritization based on online user review mining
inferredcites →https://doi.org/10.1016/j.infsof.2020.106290openalex-extracted
Identifying security issues for mobile applications based on user review summarization
inferredcites →https://doi.org/10.1016/j.infsof.2020.106466openalex-extracted
Mobile app privacy in software engineering research: A systematic mapping study
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Prioritizing user concerns in app reviews – A study of requests for new features, enhancements and bug fixes
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Exploring privacy requirements gap between developers and end users
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Introduction to Information Retrieval
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ATLAS: Automatically Detecting Discrepancies Between Privacy Policies and Privacy Labels
inferredcites →https://doi.org/10.1109/eurospw59978.2023.00022openalex-extracted
Comparing Privacy Label Disclosures of Apps Published in both the App Store and Google Play Stores
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Verification and change-impact analysis of access-control policies
inferredcites →https://doi.org/10.1109/icse.2013.6606604openalex-extracted
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inferredcites →https://doi.org/10.1109/icse.2017.55openalex-extracted
How Good Is a Security Policy against Real Breaches? A HIPAA Case Study
inferredcites →https://doi.org/10.1109/icse43902.2021.00088openalex-extracted
Identifying Key Features from App User Reviews
inferredcites →https://doi.org/10.1109/icse43902.2021.00092openalex-extracted
Automatically Matching Bug Reports With Related App Reviews
inferredcites →https://doi.org/10.1109/icse48619.2023.00114openalex-extracted
Strategies, Benefits and Challenges of App Store-inspired Requirements Elicitation
inferredcites →https://doi.org/10.1109/icsm.2015.7332475openalex-extracted
User reviews matter! Tracking crowdsourced reviews to support evolution of successful apps
inferredcites →https://doi.org/10.1109/issre5003.2020.00032openalex-extracted
An Empirical Evaluation of GDPR Compliance Violations in Android mHealth Apps
inferredcites →https://doi.org/10.1109/jsyst.2013.2260092openalex-extracted
A Systems Approach to Product Line Requirements Reuse
inferredcites →https://doi.org/10.1109/mobserv.2015.14openalex-extracted
End Users' Perception of Hybrid Mobile Apps in the Google Play Store
inferredcites →https://doi.org/10.1109/ms.2015.153openalex-extracted
Toward Data-Driven Requirements Engineering
inferredcites →https://doi.org/10.1109/msec.2019.2938445openalex-extracted
Did App Privacy Improve After the GDPR?
inferredcites →https://doi.org/10.1109/msr.2012.6224306openalex-extracted
App store mining and analysis: MSR for app stores
inferredcites →https://doi.org/10.1109/msr.2015.19openalex-extracted
The App Sampling Problem for App Store Mining
inferredcites →https://doi.org/10.1109/pic.2010.5687999openalex-extracted
A user satisfaction analysis approach for software evolution
inferredcites →https://doi.org/10.1109/re.2013.6636712openalex-extracted
User feedback in the appstore: An empirical study
inferredcites →https://doi.org/10.1109/re.2017.82openalex-extracted
Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning
inferredcites →https://doi.org/10.1109/sp.2019.00012openalex-extracted
Short Text, Large Effect: Measuring the Impact of User Reviews on Android App Security & Privacy
inferredcites →https://doi.org/10.1109/tnnls.2020.2979670openalex-extracted
A Survey of the Usages of Deep Learning for Natural Language Processing
inferredcites →https://doi.org/10.1109/tse.2007.70746openalex-extracted
Analyzing Regulatory Rules for Privacy and Security Requirements
inferredcites →https://doi.org/10.1109/tse.2016.2630689openalex-extracted
A Survey of App Store Analysis for Software Engineering
inferredcites →https://doi.org/10.1109/tse.2018.2869395openalex-extracted
Studying Bad Updates of Top Free-to-Download Apps in the Google Play Store
inferredcites →https://doi.org/10.1109/tse.2020.3030745openalex-extracted
Detecting Software Security Vulnerabilities Via Requirements Dependency Analysis
inferredcites →https://doi.org/10.1109/tse.2021.3076179openalex-extracted
Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing
inferredcites →https://doi.org/10.1109/tse.2023.3288901openalex-extracted
NLP-Based Automated Compliance Checking of Data Processing Agreements Against GDPR
inferredcites →https://doi.org/10.1109/tse.2023.3290237openalex-extracted
Privacy Engineering in the Wild: Understanding the Practitioners’ Mindset, Organizational Aspects, and Current Practices
inferredcites →https://doi.org/10.1145/1062455.1062502openalex-extracted
Verification and change-impact analysis of access-control policies
inferredcites →https://doi.org/10.1145/2568225.2568240openalex-extracted
Distilling privacy requirements for mobile applications
inferredcites →https://doi.org/10.1145/2684822.2685324openalex-extracted
Exploring the Space of Topic Coherence Measures
inferredcites →https://doi.org/10.1145/2884781.2884818openalex-extracted
Release planning of mobile apps based on user reviews
inferredcites →https://doi.org/10.1145/3132088openalex-extracted
Systematic Review of Clustering High-Dimensional and Large Datasets
inferredcites →https://doi.org/10.1145/3267808openalex-extracted
Empirical Measurement of Perceived Privacy Risk
inferredcites →https://doi.org/10.1145/3427228.3427250openalex-extracted
Measuring the Effectiveness of Privacy Policies for Voice Assistant Applications
inferredcites →https://doi.org/10.1145/3444689openalex-extracted
Natural Language Processing for Requirements Engineering
inferredcites →https://doi.org/10.1145/3482853openalex-extracted
A Brief Overview of Universal Sentence Representation Methods: A Linguistic View
inferredcites →https://doi.org/10.1145/3510003.3510201openalex-extracted
Domain-specific analysis of mobile app reviews using keyword-assisted topic models
inferredcites →https://doi.org/10.1145/3551349.3561155openalex-extracted
Unsupervised Summarization of Privacy Concerns in Mobile Application Reviews
inferredcites →https://doi.org/10.1162/tacl_a_00325openalex-extracted
Topic Modeling in Embedding Spaces
inferredcites →https://doi.org/10.18653/v1/d19-1410openalex-extracted
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
inferredcites →https://doi.org/10.2307/2529310openalex-extracted
The Measurement of Observer Agreement for Categorical Data
inferredcites →https://doi.org/10.24963/ijcai.2021/638openalex-extracted
Topic Modelling Meets Deep Neural Networks: A Survey
inferredcites →https://doi.org/10.3115/1690219.1690287openalex-extracted
Distant supervision for relation extraction without labeled data
inferredcites →https://doi.org/10.3233/978-1-61499-800-6-766openalex-extracted
An Aspect-Based Unsupervised Approach for Classifying Non-Functional Requirements on Software Reviews
inferredcites →https://doi.org/10.4135/9781071802878openalex-extracted
The Content Analysis Guidebook
inferredcites →https://doi.org/10.48550/arxiv.1802.03426openalex-extracted
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
inferredcites →https://doi.org/10.48550/arxiv.2203.05794openalex-extracted
BERTopic: Neural topic modeling with a class-based TF-IDF procedure
inferredcites →https://doi.org/10.48550/arxiv.2209.09722openalex-extracted
NLP-based Automated Compliance Checking of Data Processing Agreements against GDPR
inferredcites →https://doi.org/10.5555/1283383.1283494openalex-extracted
k-means++: the advantages of careful seeding
inferredcites →https://doi.org/10.5555/944919.944937openalex-extracted
Latent dirichlet allocation
inferredcites →https://doi.org/10.7551/mitpress/1120.003.0082openalex-extracted
Latent Dirichlet Allocation
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