Modeling Language Vagueness in Privacy Policies using Deep Neural Networks
This work addresses the issue of unclear privacy policies for users, potentially improving privacy communication, but it is incremental as it builds on existing NLP methods.
The paper tackled the problem of vague language in privacy policies by using deep neural networks to learn word vector representations, which were then visualized to discover syntactically and semantically related vague terms, showing promise for modeling language vagueness.
Website privacy policies are too long to read and difficult to understand. The over-sophisticated language makes privacy notices to be less effective than they should be. People become even less willing to share their personal information when they perceive the privacy policy as vague. This paper focuses on decoding vagueness from a natural language processing perspective. While thoroughly identifying the vague terms and their linguistic scope remains an elusive challenge, in this work we seek to learn vector representations of words in privacy policies using deep neural networks. The vector representations are fed to an interactive visualization tool (LSTMVis) to test on their ability to discover syntactically and semantically related vague terms. The approach holds promise for modeling and understanding language vagueness.