Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies
- URL: http://arxiv.org/abs/2004.11131v2
- Date: Sat, 30 Mar 2024 12:21:59 GMT
- Title: Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies
- Authors: Mukund Srinath, Shomir Wilson, C. Lee Giles,
- Abstract summary: We create PrivaSeer, a corpus of over one million English language website privacy policies.
We show results from readability tests, document similarity, keyphrase extraction, and explored the corpus through topic modeling.
- Score: 13.09699710197036
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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