# Age Matters: Analyzing Age-Related Discussions in App Reviews

arXiv: https://arxiv.org/abs/2601.21605 · HTML: https://mlsanity.com/paper/2601.21605/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 94 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
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- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [An Empirical Study of the Non-determinism of ChatGPT in Code Generation](https://mlsanity.com/paper/2308.02828/) (2023)
- [A Survey on Deep Learning for Software Engineering](https://mlsanity.com/paper/2011.14597/) (2020)
- [Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor](https://mlsanity.com/paper/2305.10374/) (2023)
- [CodeEditor: Learning to Edit Source Code with Pre-trained Models](https://mlsanity.com/paper/2210.17040/) (2022)
- [Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews](https://mlsanity.com/paper/2104.05861/) (2021)
- [APPT: Boosting Automated Patch Correctness Prediction via Fine-tuning Pre-trained Models](https://mlsanity.com/paper/2301.12453/) (2023)
- [Fairness Concerns in App Reviews: A Study on AI-based Mobile Apps](https://mlsanity.com/paper/2401.08097/) (2024)
- [A First Look at Human Values-Violation in App Reviews](https://mlsanity.com/paper/2012.10095/) (2020)
- [Studying Ad Library Integration Strategies of Top Free-to-Download Apps](https://mlsanity.com/paper/2104.00182/) (2021)
- [Protection or punishment? relating the design space of parental control apps and perceptions about them to support parenting for online safety](https://mlsanity.com/paper/2109.05347/) (2021)
- [Supporting Developers in Addressing Human-centric Issues in Mobile Apps](https://mlsanity.com/paper/2203.12212/) (2022)
- [A Study of Gender Discussions in Mobile Apps](https://mlsanity.com/paper/2303.09808/) (2023)
- [The Best Ends by the Best Means: Ethical Concerns in App Reviews](https://mlsanity.com/paper/2401.11063/) (2024)
- [Don't forget your classics: Systematizing 45 years of Ancestry for Security API Usability Recommendations](https://mlsanity.com/paper/2105.02031/) (2021)

## Abstract (arXiv)

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 insights into how mobile apps should cater to users across different age groups.We manually curated a dataset of 4,163 app reviews from the Google Play Store and identified 1,429 age-related reviews and 2,734 non-age-related reviews. We employed eight machine learning, deep learning, and large language models to automatically detect age discussions, with RoBERTa performing the best, achieving a precision of 92.46%. Additionally, a qualitative analysis of the 1,429 age-related reviews uncovers six dominant themes reflecting user concerns.