TY - CHAP
T1 - DePP: A System for Detecting Pages to Protect in Wikipedia
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
AU - Suyehira, Kelsey
AU - Spezzano, Francesca
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
UR - https://scholarworks.boisestate.edu/cs_facpubs/96
UR - https://doi.org/10.1145/2983323.2983914
UR - https://www.scopus.com/pages/publications/84996497465
U2 - 10.1145/2983323.2983914
DO - 10.1145/2983323.2983914
M3 - Chapter
T3 - 24-28-October-2016
SP - 2081
EP - 2084
BT - CIKM '16 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
Y2 - 24 October 2016 through 28 October 2016
ER -