Transparency and clear labeling of macOS applications in public marketplaces are essential for fostering user trust and enabling informed decision-making.
This research focuses on distinguishing truly free applications from those containing in-app purchases and classifying them into free, freemium, and paid categories. The analysis combines technical and language-based approaches to identify key indicators of each class, followed by classification using a supervised machine learning algorithm.
Among the evaluated models, Random Forest and Histogram-Based Gradient Boosting demonstrated the highest performance, each achieving 90% precision, 90% recall, 89% F1-score, and 90% accuracy.
These findings support the development of automated app classification systems and hold practical value for enhancing transparency within the software distribution ecosystem.
@unpublished{litvinchuk-etal-2025-report-macos-paid-apps-analysis,
author = {Zakhar Litvinchuk and Oleksandr Frankiv and Yevhenii Peteliev and Sergii Kryvoblotskyi and Nataliia Stulova},
title = {Mousetraps: Reliably distinguishing free apps and trial versions of paid macOS apps on public app marketplaces},
note = {\emph{Preprint.} \url{https://research.macpaw.com/publications/mousetraps}},
month = {May},
year = {2025}
}