Paper 1194-2021
Authors
Matthew Fruge, Noah Heinrich, Oklahoma State University
Abstract
With the advent of the global pandemic caused by the Coronavirus disease, the job market is in flux, with many people currently unemployed. These individuals are seeking employment via job posting websites such as Indeed. Online job posting websites are vulnerable to fraudulent job posts created by scammers in order to socially engineer job seekers out of private or sensitive information. This session examines job postings and attempts to automatically determine if the job posting is genuine (nonfraudulent) or fraudulent. Predictive models were used on job postings in the United States, labeling each as fraudulent or nonfraudulent based on variables provided in the data set. By using a text topic node combined with a decision tree model in SAS(r) Enterprise Miner(tm), the team achieved over a 96% accuracy rate at distinguishing whether a job posting is fraudulent or legitimate. This method could be used by sites like Indeed and Monster to flag job postings, warning the job seeker to proceed with caution until further search quality control teams can assess the posting and take it down.
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