标题： SAGE：一种从心理健康应用程序中挖掘隐私需求相关评论的上下文感知方法 显示英文标题

标题： SAGE: A Context-Aware Approach for Mining Privacy Requirements Relevant Reviews from Mental Health Apps

摘要： 心理健康（MH）应用程序通常需要敏感的用户数据，以针对心理健康需求定制服务。 然而，一些MH应用程序的数据收集实践引发了用户的重大隐私担忧。 这些担忧经常在应用评论中被提及，但其他反馈类别，如可靠性与可用性，往往更为突出。 这给自动识别可用于提取隐私需求并解决用户隐私担忧的隐私需求相关评论（隐私评论）带来了重大挑战。 因此，本研究引入了SAGE，这是一种上下文感知的方法，通过自然语言推理（NLI）从MH应用程序中自动挖掘隐私评论，使用MH领域特定的隐私假设（提供领域特定的上下文感知）和一个GPT模型（无需微调）。 在包含204K条应用评论的数据集上对SAGE进行定量评估，无需任何微调即可达到0.85的F1分数，优于微调的基线分类器BERT和T5。 此外，SAGE提取了748条之前被基于关键词的方法忽略的隐私评论，通过定性评估证明了其有效性。 这些评论随后可以被精炼为可操作的隐私需求文档。 显示英文摘要

摘要： Mental health (MH) apps often require sensitive user data to customize services for mental wellness needs. However, such data collection practices in some MH apps raise significant privacy concerns for users. These concerns are often mentioned in app reviews, but other feedback categories, such as reliability and usability, tend to take precedence. This poses a significant challenge in automatically identifying privacy requirements-relevant reviews (privacy reviews) that can be utilized to extract privacy requirements and address users' privacy concerns. Thus, this study introduces SAGE, a context-aware approach to automatically mining privacy reviews from MH apps using Natural Language Inference (NLI) with MH domain-specific privacy hypotheses (provides domain-specific context awareness) and a GPT model (eliminates the need for fine-tuning). The quantitative evaluation of SAGE on a dataset of 204K app reviews achieved an F1 score of 0.85 without any fine-tuning, outperforming the fine-tuned baseline classifiers BERT and T5. Furthermore, SAGE extracted 748 privacy reviews previously overlooked by keyword-based methods, demonstrating its effectiveness through qualitative evaluation. These reviews can later be refined into actionable privacy requirement artifacts.