E-fencing detection: mining online classified ad websites for stolen property.
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With the emergence of e-fencing, there presents a need to automate both the detection of ads selling stolen property and the reporting process for victims. This thesis presents a framework that dynamically retrieves and classifies online ads utilizing artificial intelligence (AI) to minimize amount of domain knowledge required. Evaluating these ads against existing known characteristics of theft as well as extracting new characteristics from suspicious ads. This in conjunction with a reporting system allows users to report events of theft and matches them to previously classified ads. The framework was designed such that it would be domain portable and allow for rapid adaptation to other domains. Experiments showed promising results, correctly classifying single and multiple trend datasets, displaying anomalies in price histograms, and extracting potential patterns that explain price variance. Experiments on other domains highly susceptible to scams displayed unique results contradicting some fundamental assumptions of the behavior of thieves.