Conference proceeding
Spam detection in online classified advertisements
Proceedings of the 2011 Joint WICOW/AIRWeb Workshop on web quality, pp.35-41
WebQuality '11
03/28/2011
DOI: 10.1145/1964114.1964122
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.
Details
- Title: Subtitle
- Spam detection in online classified advertisements
- Creators
- Hung TranThomas HornbeckViet Ha-ThucJames CremerPadmini Srinivasan
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 2011 Joint WICOW/AIRWeb Workshop on web quality, pp.35-41
- Series
- WebQuality '11
- DOI
- 10.1145/1964114.1964122
- Publisher
- ACM
- Language
- English
- Date published
- 03/28/2011
- Academic Unit
- Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984003007802771
Metrics
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