Effective Identification of Spam Jobs Postings Using Employer Defined Linguistic Feature
Bishwajeet Pandey, Tanisq Kala, Naman Bhoj, Hardik Gohel, Abhay Kumar, Sivaram Ponnusamy · 2022
With businesses expanding rapidly due to the integration of the internet. Hiring excellent candidates has become really essential for businesses. This has made hiring candidates online very common which has also consequently increased job scams on online job portals which critically puts the privacy of applicants at stake. Pertaining to this in this paper, we investigate the performance of various machine learning algorithms to identify job scams on online job portals based on employer defined linguistic feature. This approach would significantly help job portals to identify scam posting and make the process of identification cost effective, accurate and fast. Our proposed work achieved an accuracy of 98.679% and contributed significantly to the existing knowledge in the domain.