Spam detection in online classified advertisements
Viet-Hung Tran, Thomas Hornbeck, Viet Ha−Thuc, James F. Cremer, Padmini Srinivasan · 2011
Online classified advertisements have become an essential part of the advertisement market. Popular online classified advertisement sites such as Craigslist, Ebay Classifieds, and Oodle have attracted a huge number of posts and visits. Due to its high commercial potential, the online classified advertisement domain is a target for spammers, and this has become one of the biggest issues hindering further development of online advertisement. Therefore, spam detection in online advertisement is a crucial problem. However, previous approaches for Web spam detection in other domains do not work well in the advertisement domain. We propose a novel spam detection approach that takes into account the particular characteristics of this domain. Specifically, we propose a novel set of features that could strongly discriminate between spam and legitimate advertisement posts. Our experiments on a dataset derived from Craigslist advertisements demonstrate the effectiveness of our approach. In particular, the approach provides improvements of 55% in terms of F-1 score over a baseline that uses traditional features alone.