  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 Twitter, for 50 of the most popular apps in the world to
reveal the inclusiveness-related concerns from end users. Using a
Socio-Technical Grounded Theory approach, we analyzed 23,107 posts across the
three sources and identified 1,211 inclusiveness related posts. We organize our
empirical results in a taxonomy for inclusiveness comprising 6 major
categories: Fairness, Technology, Privacy, Demography, Usability, and Other
Human Values. To explore automated support to identifying inclusiveness-related
posts, we experimented with five state-of-the-art pre-trained large language
models (LLMs) and found that these models' effectiveness is high and yet varied
depending on the data source. GPT-2 performed best on Reddit, BERT on the
Google Play Store, and BART on Twitter. Our study provides an in-depth view of
inclusiveness-related user feedback from most popular apps and online sources.
We provide implications and recommendations that can be used to bridge the gap
between user expectations and software so that software developers can resonate
with the varied and evolving needs of the wide spectrum of users.
