Editing Behavior to Recognize Authors of Crowdsourced Content

Abstract

During this era of internet, crowd-sourcing is a very popular way of accommodating a large group of people contributing together to accomplish a goal. One of the most remarkable examples of such crowd sourced content is the Wikipedia, where millions of articles have been produced by volunteers from all over the world. Wikipedia allows anyone to edit articles without being authorized. Although creation of this huge repository of information is being possible because of the freedom of editing, it also attracts sock puppets and malicious users to cause ruthless destruction in Wikipedia contents. One way of dealing with such malevolent users is to predict the identity of ambiguous authors. However, authorship recognition in collaborative environment like Wikipedia is very challenging. In this paper, we propose a novel way of mapping ambiguous users identity to previously known users based on their editing profile. The proposed editing behavior based authorship recognition can be applied to decide on trusty and offensive authors, identity theft, shock puppetry, human behavior analysis, and so on. Our experimentation on a large database of Wikipedia demonstrate promising results of using editing behavior to recognize authors of collaborative writing.