分析应用评论中的年龄议题，帮助开发者更好适配不同年龄段用户需求。
Age Matters: Analyzing Age-Related Discussions in App Reviews
- 从4163条应用评论中识别出1429条与年龄相关的内容。
- 用RoBERTa模型检测年龄话题，准确率达92.46%。
- 发现六类年龄相关痛点，适合做适老化或青少年版应用设计。
近年来，移动应用已成为管理生活各方面的不可或缺工具，从提升效率到提供个性化娱乐，深刻改变了人们日常习惯。然而，这些应用在满足不同年龄群体需求方面仍存在差距。不同年龄段的用户面临独特挑战：年轻用户可能遭遇不适宜内容，年长用户则因视觉和认知能力下降而难以操作。尽管已有推动年龄包容性设计的努力，但对用户视角下年龄相关问题的理解有限，制约了开发者的有效应对。本研究通过分析应用评论中的年龄议题，探索如何让移动应用更好地服务跨年龄用户。研究手动构建了包含4,163条谷歌应用商店评论的数据集，其中1,429条为年龄相关评论，2,734条为非年龄相关评论。采用八种机器学习、深度学习及大语言模型进行自动检测，结果表明RoBERTa表现最佳，精确率达92.46%。进一步定性分析揭示了六类主导主题，反映用户在使用中的实际关切。
原文摘要 · Abstract (English)
In recent years, mobile applications have become indispensable tools for managing various aspects of life. From enhancing productivity to providing personalized entertainment, mobile apps have revolutionized people's daily routines. Despite this rapid growth and popularity, gaps remain in how these apps address the needs of users from different age groups. Users of varying ages face distinct challenges when interacting with mobile apps, from younger users dealing with inappropriate content to older users having difficulty with usability due to age-related vision and cognition impairments. Although there have been initiatives to create age-inclusive apps, a limited understanding of user perspectives on age-related issues may hinder developers from recognizing specific challenges and implementing effective solutions. In this study, we explore age discussions in app reviews to gain insights into how mobile apps should cater to users across different age groups.We manually curated a dataset of 4,163 app reviews from the Google Play Store and identified 1,429 age-related reviews and 2,734 non-age-related reviews. We employed eight machine learning, deep learning, and large language models to automatically detect age discussions, with RoBERTa performing the best, achieving a precision of 92.46%. Additionally, a qualitative analysis of the 1,429 age-related reviews uncovers six dominant themes reflecting user concerns.
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