Security and Privacy Taxonomy Generation from Mobile App Reviews
2026-08-10 • Computation and Language
Computation and Language
AI summaryⓘ
The authors looked at millions of mobile app reviews to better understand user concerns about privacy and security. They created a method called TaxoScale that can automatically organize these reviews into a detailed and large-scale category system, which is hard for traditional methods to do. Their approach uses smart grouping and AI-based labeling to build a taxonomy that captures more new and relevant topics than older methods. This helps keep up with changing user worries in app reviews.
taxonomymobile app reviewsprivacysecuritylarge language models (LLM)recursive hierarchical clusteringnatural language processingautomated classificationdata scalability
Authors
Moghis Fereidouni, Vinaik Chhetri, Umar Farooq, A. B. Siddique
Abstract
Mobile app reviews are a rich, continuously renewing source of how users experience privacy and security, yet existing taxonomies of these concerns are hand-crafted and cannot keep pace with the evolving nature of the data. Automating taxonomy construction is the natural response, but scalability is the core challenge: current LLM- and clustering-based methods are developed for scientific corpora of a few thousand documents and do not extend to app review collections numbering in the hundreds of thousands. We address this gap in two ways. First, we filter app reviews for privacy- and security-related content, yielding a comprehensive corpus of over 600K reviews. Second, we introduce TaxoScale, a pipeline that handles taxonomy construction at this scale by extending an expert-defined taxonomy via Recursive Hierarchical Clustering and LLM-based node naming. TaxoScale outperforms strong automatic-taxonomy baselines on path, level, coverage, and novelty metrics, and discovers novel branches absent from prior taxonomies.