We present Health App Reviews for Privacy & Trust (HARPT), a large-scale annotated corpus of user reviews from patient portal and telehealth applications (apps) aimed at advancing research in user privacy and trust. The dataset comprises 480,450 user reviews labeled across seven classes that capture critical aspects of trust in applications, trust in providers, and privacy concerns. Our multistage strategy integrated keyword-based filtering, iterative manual labeling with review, targeted data augmentation, and weak supervision using transformer-based classifiers. In parallel, we manually annotated a curated subset of 7,000 reviews to support the development and evaluation of machine learning models. We benchmarked a broad range of models, providing a baseline for future work. HARPT is released under an open resource license to support reproducible research in usable privacy, trust and health informatics.
Kelly et al. (Wed,) studied this question.
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