Spam detection in online classified advertisements

Abstract

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.