mlsanityData to 14 Oct 2026

Age Matters: Analyzing Age-Related Discussions in App Reviews

arXiv 2601.21605 ↗First posted 29 Jan 2026cs.SE cs.HC cs.LGcited by 0

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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.

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22 of 94 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  5. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  6. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  7. 2023A Survey on Evaluation of Large Language Modelscited by 1,242
  8. 2018Universal Sentence Encodercited by 489
  9. 2023An Empirical Study of the Non-determinism of ChatGPT in Code Generationcited by 137
  10. 2020A Survey on Deep Learning for Software Engineeringcited by 38
  11. 2023Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Laborcited by 18
  12. 2022CodeEditor: Learning to Edit Source Code with Pre-trained Modelscited by 12
10 more
  1. 2021Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviewscited by 7
  2. 2023APPT: Boosting Automated Patch Correctness Prediction via Fine-tuning Pre-trained Modelscited by 4
  3. 2024Fairness Concerns in App Reviews: A Study on AI-based Mobile Appscited by 4
  4. 2020A First Look at Human Values-Violation in App Reviewscited by 3
  5. 2021Studying Ad Library Integration Strategies of Top Free-to-Download Appscited by 3
  6. 2021Protection or punishment? relating the design space of parental control apps and perceptions about them to support parenting for online safetycited by 3
  7. 2022Supporting Developers in Addressing Human-centric Issues in Mobile Appscited by 2
  8. 2023A Study of Gender Discussions in Mobile Appscited by 2
  9. 2024The Best Ends by the Best Means: Ethical Concerns in App Reviewscited by 2
  10. 2021Don't forget your classics: Systematizing 45 years of Ancestry for Security API Usability Recommendationscited by 1

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