Predicting Outcome for Collaborative Featured Article Nomination in Wikipedia

Abstract

In Wikipedia, good articles are wanted. While Wikipedia relies on collaborative effort from online volunteers for quality checking, the process of selecting top quality articles is time consuming. At present, the duty of decision making is shouldered by only a couple of administrators. Aiming to assist in the quality checking cycles so as to cope with the exponential growth of online contributions to Wikipedia, this work studies the task of predicting the outcome of featured article (FA) nominations.  We analyze FA candidate (FAC) sessions collected over a period of 3.5 years, and examine the extent to which consensus has been practised in this process.  We explore the use of interaction features between FAC reviewers to learn SVM classifiers to predict the nomination outcome.  We find that, calibrating the individual user's polarity of opinions as features improves the prediction accuracy significantly.