Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
The nature of statistical learning theory
The nature of statistical learning theory
A Study of Approaches to Hypertext Categorization
Journal of Intelligent Information Systems
Machine Learning
Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Focused Crawling Using Context Graphs
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Text categorization based on k-nearest neighbor approach for web site classification
Information Processing and Management: an International Journal
Web site mining: a new way to spot competitors, customers and suppliers in the world wide web
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Building trust in online auction markets through an economic incentive mechanism
Decision Support Systems
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Information Systems Research
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Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
Topical web crawlers: Evaluating adaptive algorithms
ACM Transactions on Internet Technology (TOIT)
Electronic Commerce Fraud: Towards an Understanding of the Phenomenon
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 07
Information Systems Research
Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model
Information Systems Research
Applying Authorship Analysis to Extremist-Group Web Forum Messages
IEEE Intelligent Systems
Journal of the American Society for Information Science and Technology
From fingerprint to writeprint
Communications of the ACM - Supporting exploratory search
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
Detecting semantic cloaking on the web
Proceedings of the 15th international conference on World Wide Web
A survey of trust and reputation systems for online service provision
Decision Support Systems
Information Systems Research
ACM Transactions on Information Systems (TOIS)
Weblog classification for fast splog filtering: a URL language model segmentation approach
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Detecting Fake Medical Web Sites Using Recursive Trust Labeling
ACM Transactions on Information Systems (TOIS)
Information Technology and Management
Information Technology and Management
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The ability to automatically detect fraudulent escrow websites is important in order to alleviate online auction fraud. Despite research on related topics, such as web spam and spoof site detection, fake escrow website categorization has received little attention. The authentic appearance of fake escrow websites makes it difficult for Internet users to differentiate legitimate sites from phonies; making systems for detecting such websites an important endeavor. In this study we evaluated the effectiveness of various features and techniques for detecting fake escrow websites. Our analysis included a rich set of fraud cues extracted from web page text, image, and link information. We also compared several machine learning algorithms, including support vector machines, neural networks, decision trees, naïve bayes, and principal component analysis. Experiments were conducted to assess the proposed fraud cues and techniques on a test bed encompassing nearly 90,000 web pages derived from 410 legitimate and fake escrow websites. The combination of an extended feature set and a support vector machines ensemble classifier enabled accuracies over 90 and 96% for page and site level classification, respectively, when differentiating fake pages from real ones. Deeper analysis revealed that an extended set of fraud cues is necessary due to the broad spectrum of tactics employed by fraudsters. The study confirms the feasibility of using automated methods for detecting fake escrow websites. The results may also be useful for informing existing online escrow fraud resources and communities of practice about the plethora of fraud cues pervasive in fake websites.