Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies
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arXiv
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| Format: | Preprint |
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2020
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| _version_ | 1866910391020290048 |
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| author | Srinath, Mukund Wilson, Shomir Giles, C. Lee |
| author_facet | Srinath, Mukund Wilson, Shomir Giles, C. Lee |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2004_11131 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies Srinath, Mukund Wilson, Shomir Giles, C. Lee Information Retrieval Cryptography and Security 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. |
| title | Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies |
| topic | Information Retrieval Cryptography and Security |
| url | https://arxiv.org/abs/2004.11131 |