DePP

Abstract

Wikipedia is based on the idea that anyone can make edits to the website in order to create reliable and crowd-sourced content. Yet with the cover of internet anonymity, some users make changes to the website that do not align with Wikipedia's intended uses. For this reason, Wikipedia allows for some pages of the website to become protected, where only certain users can make revisions to the page. This allows administrators to protect pages from vandalism, libel, and edit wars. However, with over five million pages on Wikipedia, it is impossible for administrators to monitor all pages and manually enforce page protection. In this paper we consider for the first time the problem of deciding whether a page should be protected or not in a collaborative environment such as Wikipedia. We formulate the problem as a binary classification task and propose a novel set of features to decide which pages to protect based on (i) users page revision behavior and (ii) page categories. We tested our system, called DePP, on a new dataset we built consisting of 13.6K pages (half protected and half unprotected) and 1.9M edits. Experimental results show that DePP reaches 93.24% classification accuracy and significantly improves over baselines.

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