With the increasing popularity of mobile devices and the wide adoption of mobile Apps, an increasing concern of privacy issues is raised. Privacy policy is identified as a proper medium to indicate the legal terms, such as GDPR, and to bind legal agreement between service providers and users. However, privacy policies are usually long and vague for end users to read and understand. It is thus important to be able to automatically analyze the document structures of privacy policies to assist user understanding. In this work we create a manually labelled corpus containing 167 privacy policies (of more than 447K words and 5,276 annotated paragraphs). We report the annotation process and details of the annotated corpus. We also benchmark our data corpus with 4 document classification models, thoroughly analyze the results and discuss challenges and opportunities for the research committee to use the corpus. We release our labelled corpus as well as the classification models for public access.
@article{arxiv.2005.06945,
title = {APPCorp: A Corpus for Android Privacy Policy Document Structure Analysis},
author = {Shuang Liu and Renjie Guo and Baiyang Zhao and Tao Chen and Meishan Zhang},
journal= {arXiv preprint arXiv:2005.06945},
year = {2020}
}