Privacy Now or Never: Large-Scale Extraction and Analysis of Dates in Privacy Policy Text

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Abstract

The General Data Protection Regulation (GDPR) and other recent privacy laws require organizations to post their privacy policies, and place specific expectations on organisations' privacy practices. Privacy policies take the form of documents written in natural language, and one of the expectations placed upon them is that they remain up to date. To investigate legal compliance with this recency requirement at a large scale, we create a novel pipeline that includes crawling, regex-based extraction, candidate date classification and date object creation to extract updated and effective dates from privacy policies written in English. We then analyze patterns in policy dates using four web crawls and find that only about 40% of privacy policies online contain a date, thereby making it difficult to assess their regulatory compliance. We also find that updates in privacy policies are temporally concentrated around passage of laws regulating digital privacy (such as the GDPR), and that more popular domains are more likely to have policy dates as well as more likely to update their policies regularly.

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Srinath, M., Matheson, L., Venkit, P. N., Zanfir-Fortuna, G., Schaub, F., Lee Giles, C., & Wilson, S. (2023). Privacy Now or Never: Large-Scale Extraction and Analysis of Dates in Privacy Policy Text. In DocEng 2023 - Proceedings of the 2023 ACM Symposium on Document Engineering. Association for Computing Machinery, Inc. https://doi.org/10.1145/3573128.3609342

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