In an era of rapidly expanding software usage, catering to the diverse needs of users from various backgrounds has become a critical challenge. Inclusiveness, representing a core human value, is frequently overlooked during software development, leading to user dissatisfaction. Users often engage in discourse on online platforms where they indicate their concerns. In this study, we leverage user feedback from three popular online sources Reddit, Google Play Store, and X, for 50 of the most popular apps in the world. Using a Socio-Technical Grounded Theory approach, we analyzed 22,000 posts across the three sources. We organize our empirical results in a taxonomy for inclusiveness comprising 5 major categories: Algorithmic Bias, Technology, Demography, Accessibility, and Other Human Values. To explore automated support for identifying inclusiveness-related posts, we experimented with a large language model (GPT4o-mini) and found that it is capable of identifying inclusiveness-related user feedback. We provide implications and recommendations that can help software practitioners to better identify inclusiveness issues to support a wider range of users

翻译：在软件使用迅速扩张的时代，满足不同背景用户的多样化需求已成为一项关键挑战。包容性作为一项核心人类价值，在软件开发过程中常被忽视，从而导致用户不满。用户经常在在线平台上发表评论，表达他们的关切。本研究利用来自三个流行在线平台（Reddit、Google Play商店和X）的用户反馈，针对全球50款最受欢迎的应用程序展开分析。采用社会技术扎根理论方法，我们分析了这三个来源共计22,000条帖子。我们将实证研究结果组织成一个包含五大类别的包容性分类体系：算法偏见、技术、人口统计学、可访问性以及其他人类价值。为探索自动识别包容性相关帖子的技术支持，我们实验了大型语言模型（GPT4o-mini），发现其能够有效识别与包容性相关的用户反馈。我们提出了可帮助软件从业者更好地识别包容性问题、从而支持更广泛用户群体的启示与建议。