Identifying Relevant Text Fragments to Help Crowdsource Privacy Policy Annotations

Authors

  • Rohan Ramanath Carnegie Mellon University
  • Florian Schaub Carnegie Mellon University
  • Shomir Wilson Carnegie Mellon University
  • Fei Liu Carnegie Mellon University
  • Norman Sadeh Carnegie Mellon University
  • Noah Smith Carnegie Mellon University

Abstract

In today's age of big data, websites are collecting an increasingly wide variety of information about their users. The texts of websites' privacy policies, which serve as legal agreements between service providers and users, are often long and difficult to understand. Automated analysis of those texts has the potential to help users better understand the implications of agreeing to such policies. In this work, we present a technique that combines machine learning and crowdsourcing to semi-automatically extract key aspects of website privacy policies that is scalable, fast, and cost-effective.

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Published

2014-09-05

How to Cite

Ramanath, R., Schaub, F., Wilson, S., Liu, F., Sadeh, N., & Smith, N. (2014). Identifying Relevant Text Fragments to Help Crowdsource Privacy Policy Annotations. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1). Retrieved from https://ojs.aaai.org/index.php/HCOMP/article/view/13179