An investigation of crowdsourcing methods in enhancing the machine learning approach for detecting online recruitment fraud
Krishnadas Nanath, Liting Olney
Abstract
Misinformation on the web has become a problem of significant impact in an information-driven society. Persistent and large volumes of fake content are being injected, and hence the content (news, articles, jobs, facts) available online is often questionable. This research reviews a range of machine learning algorithms to tackle a specific case of online recruitment fraud (ORF). A model with content features of job posting is tested with five supervised machine learning (ML) algorithms. It then investigates various crowdsourcing techniques that could enhance prediction accuracy and add human insights to machine learning automation. Each crowdsourcing method (explored as human signals online) was tested across the same ML algorithms to test its effectiveness in predicting fake job postings. The testing was conducted by comparing the hybrid models of machine learning and crowdsourced inputs. This study revealed that the best ML algorithm was different in the automated model compared to the hybrid model. Results also indicated that the net promoter type crowdsourced question resulted in the best accuracy in classifying fraudulent and legitimate jobs. The decision tree and generalized linear model demonstrated the highest accuracy among all the tested models.