Unveiling Inclusiveness-Related User Feedback in Mobile Applications
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916499940179968 |
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| author | Arony, Nowshin Nawar Li, Ze Shi Damian, Daniela Xu, Bowen |
| author_facet | Arony, Nowshin Nawar Li, Ze Shi Damian, Daniela Xu, Bowen |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_00984 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unveiling Inclusiveness-Related User Feedback in Mobile Applications Arony, Nowshin Nawar Li, Ze Shi Damian, Daniela Xu, Bowen Software Engineering 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 |
| title | Unveiling Inclusiveness-Related User Feedback in Mobile Applications |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2311.00984 |