  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.
