@InProceedings{mysoresathyendra-EtAl:2017:EMNLP2017,
  author    = {Mysore Sathyendra, Kanthashree  and  Wilson, Shomir  and  Schaub, Florian  and  Zimmeck, Sebastian  and  Sadeh, Norman},
  title     = {Identifying the Provision of Choices in Privacy Policy Text},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2774--2779},
  abstract  = {Websites' and mobile apps' privacy policies, written in natural language, tend
	to be long and difficult to understand. Information privacy revolves around the
	fundamental principle of Notice and choice, namely the idea that users should
	be able to make informed decisions about what information about them can be
	collected and how it can be used. Internet users want control over their
	privacy, but their choices are often hidden in long and convoluted privacy
	policy texts. Moreover, little (if any) prior work has been done to detect the
	provision of choices in text. We address this challenge of enabling user choice
	by automatically identifying and extracting pertinent choice language in
	privacy policies. In particular, we present a two-stage architecture of
	classification models to identify opt-out choices in privacy policy text,
	labelling common varieties of choices with a mean F1 score of 0.735. Our
	techniques enable the creation of systems to help Internet users to learn about
	their choices, thereby effectuating notice and choice and improving Internet
	privacy.},
  url       = {https://www.aclweb.org/anthology/D17-1294}
}