# A Decade of Privacy-Relevant Android App Reviews: Large Scale Trends

arXiv: https://arxiv.org/abs/2403.02292 · HTML: https://mlsanity.com/paper/2403.02292/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 100 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021)
- [Building Machine Translation Systems for the Next Thousand Languages](https://mlsanity.com/paper/2205.03983/) (2022)
- [Android Permissions Remystified: A Field Study on Contextual Integrity](https://mlsanity.com/paper/1504.03747/) (2015)
- [Ethical Frameworks and Computer Security Trolley Problems: Foundations for Conversations](https://mlsanity.com/paper/2302.14326/) (2023)
- [Evaluating the Information Security Awareness of Smartphone Users](https://mlsanity.com/paper/1906.10229/) (2019)
- [SoK: A Framework for Unifying At-Risk User Research](https://mlsanity.com/paper/2112.07047/) (2021)
- [A World Full of Privacy and Security (Mis)conceptions? Findings of a Representative Survey in 12 Countries](https://mlsanity.com/paper/2212.10382/) (2022)
- [An Analysis of Pre-installed Android Software](https://mlsanity.com/paper/1905.02713/) (2019)
- [Why Older Adults (Don't) Use Password Managers](https://mlsanity.com/paper/2010.01973/) (2020)
- [The Android Update Problem: An Empirical Study](https://mlsanity.com/paper/1801.02716/) (2018)
- ["Should I Worry?" A Cross-Cultural Examination of Account Security Incident Response](https://mlsanity.com/paper/1808.08177/) (2018)
- ["All of them claim to be the best": Multi-perspective study of VPN users and VPN providers](https://mlsanity.com/paper/2208.03505/) (2022)
- ["If sighted people know, I should be able to know:" Privacy Perceptions of Bystanders with Visual Impairments around Camera-based Technology](https://mlsanity.com/paper/2210.12232/) (2022)
- [How WEIRD is Usable Privacy and Security Research? (Extended Version)](https://mlsanity.com/paper/2305.05004/) (2023)

## Built on by (most cited first, 2 of 2)

- [What Users Value and Critique: Large-Scale Analysis of User Feedback on AI-Powered Mobile Apps](https://mlsanity.com/paper/2506.10785/) (2025)
- [Security and Privacy Taxonomy Generation from Mobile App Reviews](https://mlsanity.com/paper/2608.09049/) (2026)

## Abstract (arXiv)

We present an analysis of 12 million instances of privacy-relevant reviews publicly visible on the Google Play Store that span a 10 year period. By leveraging state of the art NLP techniques, we examine what users have been writing about privacy along multiple dimensions: time, countries, app types, diverse privacy topics, and even across a spectrum of emotions. We find consistent growth of privacy-relevant reviews, and explore topics that are trending (such as Data Deletion and Data Theft), as well as those on the decline (such as privacy-relevant reviews on sensitive permissions). We find that although privacy reviews come from more than 200 countries, 33 countries provide 90% of privacy reviews. We conduct a comparison across countries by examining the distribution of privacy topics a country's users write about, and find that geographic proximity is not a reliable indicator that nearby countries have similar privacy perspectives. We uncover some countries with unique patterns and explore those herein. Surprisingly, we uncover that it is not uncommon for reviews that discuss privacy to be positive (32%); many users express pleasure about privacy features within apps or privacy-focused apps. We also uncover some unexpected behaviors, such as the use of reviews to deliver privacy disclaimers to developers. Finally, we demonstrate the value of analyzing app reviews with our approach as a complement to existing methods for understanding users' perspectives about privacy