Organisations disclose their privacy practices by posting privacy policies on their website. Even though users often care about their digital privacy, they often don't read privacy policies since they require a significant investment in time and effort. Although natural language processing can help in privacy policy understanding, there has been a lack of large scale privacy policy corpora that could be used to analyse, understand, and simplify privacy policies. Thus, we create PrivaSeer, a corpus of over one million English language website privacy policies, which is significantly larger than any previously available corpus. We design a corpus creation pipeline which consists of crawling the web followed by filtering documents using language detection, document classification, duplicate and near-duplication removal, and content extraction. We investigate the composition of the corpus and show results from readability tests, document similarity, keyphrase extraction, and explored the corpus through topic modeling.
@article{arxiv.2004.11131,
title = {Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies},
author = {Mukund Srinath and Shomir Wilson and C. Lee Giles},
journal= {arXiv preprint arXiv:2004.11131},
year = {2024}
}