自动构建百万级应用评论中的安全隐私分类体系，发现新问题类型。
Security and Privacy Taxonomy Generation from Mobile App Reviews
- 用过滤+递归聚类+大模型命名，实现超大规模评论分类
- 处理60万条评论，发现现有分类中缺失的新风险类别
- 适合做隐私安全研究、应用风控的开发者与研究人员
移动应用评论是用户感知隐私与安全问题的丰富持续来源，但现有分类体系依赖人工构建，难以跟上数据演变。自动化构建是自然方向，但面临可扩展性挑战：当前基于大模型和聚类的方法仅适用于数千篇科学文献，无法处理数以十万计的应用评论。本文通过两种方式填补这一空白：首先，筛选出超过60万条与隐私和安全相关的应用评论，构建全面语料库；其次，提出TaxoScale流水线，通过递归分层聚类和大模型节点命名，从专家定义的初始分类出发扩展生成大规模分类体系。TaxoScale在路径、层级、覆盖率和新颖性等指标上优于主流自动分类基线，并发现了先前分类中缺失的新分支。
原文摘要 · Abstract (English)
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.
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