Fake Job Posting Detection Using Machine Learning

Tawfiqul Islam Talukder; S.M Shouvik Islam

Fake Job Posting Detection Using Machine Learning

creativework.keywords	Fake Job, Online Recruitment, Machine Learning, Ensemble Approach
dc.contributor.advisor	Dr. Mohammad Monirujjaman Khan
dc.contributor.author	Tawfiqul Islam Talukder
dc.contributor.author	S.M Shouvik Islam
dc.contributor.id	1521092042
dc.contributor.id	1712767642
dc.coverage.department	Electrical and Computer Engineering
dc.date.accessioned	2025-12-23
dc.date.accessioned	2025-12-23T09:04:58Z
dc.date.available	2025-12-23T09:04:58Z
dc.date.issued	2022-12-30
dc.description.abstract	This report presents the design and the implementation of a system that can detect fake jobs using a machine learning method that employs a variety of categorization algorithms. The COVID-19 epidemic situation has transformed the regular livelihoods of mankind in the world. This epidemic has put excessive pressure on the job market. As a consequence of the epidemic, most organizations have halted their recruiting processes, which has raised the rate of unemployment. Online recruiting has suddenly increased the quantity of applicants while also bridging the distance between recruiters and candidates. It indicates that scammers have emerged in the online recruiting market. They provide extremely high pay ranges or any other type of benefit on several online platforms. It's called "Fake Job Postings." Job seekers are applying for those fake jobs. As a result, scammers steal their personal information. Scammers use their personal information for a variety of cybercrimes or sell it on the dark web. This paper's objective is to identify and verify these job advertisements, whether they’re fake or not. To identify these fake job advertisements, Machine Learning Algorithms (MLA) was implemented throughout this study, such as the Random Forest algorithm, and Logistic Regression algorithm. This study trained and tested the dataset and got an accuracy of 98.86 percent in Logistic Regression and 98.54 percent in Random Forest. In the Logistic Regression algorithm, our recommended technique has an accuracy of 98.86 percent, which is a huge improvement over the current methods. The accuracy percentile of both the algorithms used throughout this analysis is substantially in excess of prior studies, showing that the algorithms utilized throughout this analysis are well balanced.
dc.identifier.cd	600000293
dc.identifier.uri	https://repository.northsouth.edu/handle/123456789/1550
dc.publisher	North South University
dc.rights	@ NSU Library
dc.title	Fake Job Posting Detection Using Machine Learning
dc.type	Project