Mobile software apps ("apps") are one of the prevailing digital technologies that our modern life heavily depends on. A key issue in the development of apps is how to design gender-inclusive apps. Apps that do not consider gender inclusion, diversity, and equality in their design can create barriers (e.g., excluding some of the users because of their gender) for their diverse users. While there have been some efforts to develop gender-inclusive apps, a lack of deep understanding regarding user perspectives on gender may prevent app developers and owners from identifying issues related to gender and proposing solutions for improvement. Users express many different opinions about apps in their reviews, from sharing their experiences, and reporting bugs, to requesting new features. In this study, we aim at unpacking gender discussions about apps from the user perspective by analysing app reviews. We first develop and evaluate several Machine Learning (ML) and Deep Learning (DL) classifiers that automatically detect gender reviews (i.e., reviews that contain discussions about gender). We apply our ML and DL classifiers on a manually constructed dataset of 1,440 app reviews from the Google App Store, composing 620 gender reviews and 820 non-gender reviews. Our best classifier achieves an F1-score of 90.77%. Second, our qualitative analysis of a randomly selected 388 out of 620 gender reviews shows that gender discussions in app reviews revolve around six topics: App Features, Appearance, Content, Company Policy and Censorship, Advertisement, and Community. Finally, we provide some practical implications and recommendations for developing gender-inclusive apps.

翻译：移动软件应用（"应用"）是现代生活高度依赖的主流数字技术之一。应用开发的关键议题在于如何设计具有性别包容性的应用。未在设计中考虑性别包容性、多样性与平等的应用，会对其多元化用户群体造成障碍（例如因用户性别将其排除在外）。尽管已有一些开发性别包容性应用的努力，但缺乏对用户性别观点的深入理解，可能阻碍应用开发者和所有者识别与性别相关的问题并提出改进方案。用户在应用评论中表达了对应用的多元观点，包括分享体验、报告缺陷以及提出功能需求等。本研究旨在通过分析应用评论，从用户视角揭示关于应用的性别讨论。我们首先开发并评估了多种机器学习（ML）和深度学习（DL）分类器，用于自动检测性别相关评论（即包含性别讨论的评论）。我们在手动构建的1,440条谷歌应用商店评论数据集（包含620条性别评论与820条非性别评论）上应用了ML和DL分类器，最佳分类器实现了90.77%的F1分数。其次，我们对620条性别评论中随机抽取的388条进行定性分析，发现应用评论中的性别讨论围绕六大主题展开：应用功能、外观设计、内容、公司政策与审查、广告推广、社区互动。最后，我们为开发性别包容性应用提出了若干实践启示与建议。