@inproceedings{f6c3cda3c62947f6bce5d490774a6a93,
title = "Identifying Relevant Text Fragments to Help Crowdsource Privacy Policy Annotations",
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.",
author = "Rohan Ramanath and Florian Schaub and Shomir Wilson and Fei Liu and Norman Sadeh and Smith, {Noah A.}",
note = "Publisher Copyright: {\textcopyright} HCOMP 2014. All rights reserved.; 2nd AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2014 ; Conference date: 02-11-2014 Through 04-11-2014",
year = "2014",
month = nov,
day = "5",
language = "English (US)",
series = "Proceedings of the 2nd AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2014",
publisher = "AAAI press",
pages = "54--55",
editor = "Bigham, {Jeffrey P.} and David Parkes",
booktitle = "Proceedings of the 2nd AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2014",
}