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Reseach Article

Online Recruitment Fraud Detection: A Machine Learning-based Model for Ghanaian Job Websites

by Delali Kwasi Dake
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 51
Year of Publication: 2023
Authors: Delali Kwasi Dake
10.5120/ijca2023922639

Delali Kwasi Dake . Online Recruitment Fraud Detection: A Machine Learning-based Model for Ghanaian Job Websites. International Journal of Computer Applications. 184, 51 ( Mar 2023), 20-28. DOI=10.5120/ijca2023922639

@article{ 10.5120/ijca2023922639,
author = { Delali Kwasi Dake },
title = { Online Recruitment Fraud Detection: A Machine Learning-based Model for Ghanaian Job Websites },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2023 },
volume = { 184 },
number = { 51 },
month = { Mar },
year = { 2023 },
issn = { 0975-8887 },
pages = { 20-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number51/32652-2023922639/ },
doi = { 10.5120/ijca2023922639 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:35.916913+05:30
%A Delali Kwasi Dake
%T Online Recruitment Fraud Detection: A Machine Learning-based Model for Ghanaian Job Websites
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 51
%P 20-28
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The proliferation of online job websites has eased the difficulties in hiring and applying for jobs globally. Unfortunately, the risk of defrauding desperate job seekers exists with malicious recruiters taking advantage of the loopholes in the online recruitment process. The reactive approach to detecting online job fraud and the subsequent warnings on reputable job websites hasn't curtailed this spiteful act. The purpose of the study is to propose a machine learning model for proactive job fraud detection. In building the predictive model, a job fraud dataset from a job advertisement firm in Ghana was utilised. Using the 10-fold and the 5-fold cross-validation techniques, a job fraud detection model was built by comparing conventional and ensemble machine learning algorithms. The machine learning metrics, including accuracy, F1-score and the area under the curve (AUC) value, were reported and discussed. The findings show that the Random Forest traditional algorithm, with an accuracy of 91.86%, is best suited for the dataset. The investigation further indicates that information gain and chi-square feature selection mechanisms decreased classification accuracy marginally to 91.51%.

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Index Terms

Computer Science
Information Sciences

Keywords

Supervised Algorithm Ensemble Learning Job Fraud Employment Scam Online Recruitment Fraud