从移动应用评论生成安全与隐私分类体系
Security and Privacy Taxonomy Generation from Mobile App Reviews
- University of Kentucky（肯塔基大学）
- Louisiana State University（路易斯安那州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有安全隐私分类体系手工构建、无法适配海量应用评论的问题，提出TaxoScale流程，筛选60万条相关评论构建语料库，其在多指标上优于基线且发现新分支。
AI中文摘要：
移动应用评论是反映用户隐私与安全体验的丰富且持续更新的来源，但现有的这些关注的分类体系是手工构建的，无法跟上数据的演变性质。自动构建分类体系是自然的应对方式，但可扩展性是核心挑战：当前基于大语言模型（LLM）和聚类的方法是为数千份文档的科学语料库开发的，无法扩展到数十万条的应用评论集合。我们通过两种方式解决这一差距：首先，筛选应用评论中与隐私和安全相关的内容，得到超过60万条评论的综合语料库；其次，引入TaxoScale，这是一种通过递归层次聚类和基于LLM的节点命名，在该规模下处理分类体系构建的流程。TaxoScale在路径、层级、覆盖范围和新颖性指标上优于强大的自动分类基线，且发现了现有分类体系中不存在的新分支。
英文摘要：
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.
