Unveiling Inclusiveness-Related User Feedback in Mobile Applications
Nowshin Nawar Arony, Ze Shi Li, Daniela Damian, Bowen Xu

TL;DR
This paper analyzes user feedback from online platforms to identify inclusiveness issues in mobile apps, proposing a taxonomy and demonstrating the potential of large language models for automated detection.
Contribution
It introduces a taxonomy of inclusiveness concerns and evaluates GPT-4 based models for automated identification of related user feedback.
Findings
Identified five major categories of inclusiveness issues.
GPT-4 can effectively detect inclusiveness-related feedback.
Provides practical recommendations for developers.
Abstract
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…
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Taxonomy
TopicsSocial Media and Politics · Impact of Technology on Adolescents · Child Development and Digital Technology
