Age Matters: Analyzing Age-Related Discussions in App Reviews
Shashiwadana Nirmania, Garima Sharma, Hourieh Khalajzadeh, Mojtaba Shahin

TL;DR
This paper investigates how mobile app reviews reveal age-related user concerns, using machine learning models to automatically detect age discussions and analyzing themes to inform age-inclusive app design.
Contribution
It introduces a dataset of app reviews, applies advanced models for automatic detection of age-related discussions, and provides qualitative insights into user concerns across age groups.
Findings
RoBERTa achieved 92.46% precision in detecting age-related reviews.
Six dominant themes of user concerns were identified from qualitative analysis.
A dataset of 4,163 app reviews was curated for analysis.
Abstract
In recent years, mobile applications have become indispensable tools for managing various aspects of life. From enhancing productivity to providing personalized entertainment, mobile apps have revolutionized people's daily routines. Despite this rapid growth and popularity, gaps remain in how these apps address the needs of users from different age groups. Users of varying ages face distinct challenges when interacting with mobile apps, from younger users dealing with inappropriate content to older users having difficulty with usability due to age-related vision and cognition impairments. Although there have been initiatives to create age-inclusive apps, a limited understanding of user perspectives on age-related issues may hinder developers from recognizing specific challenges and implementing effective solutions. In this study, we explore age discussions in app reviews to gain…
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Taxonomy
TopicsTechnology Use by Older Adults · Innovative Human-Technology Interaction · Personal Information Management and User Behavior
