Green

In this paper, we address the problem of identifying spam users on Wikipedia and present our preliminary results. We formulate the problem as a binary classification task and propose a set of features based on user editing behavior to separate spammers from benign users. We tested our system on a new dataset we built consisting of 4.2K (half spam and half benign) users and 75.6K edits. Experimental results show that our approach reaches 80.8% classification accuracy and 0.88 mean average precision. We compared against ORES, the most recent tool developed by Wikimedia which assigns a damaging score to each edit, and we show that our system outperforms ORES in spam users detection.