Fake Job Detection and Analysis Using Machine Learning and Deep Learning Algorithms

Abstract

With the pandemic situation, there is a strong rise in the number of online jobs posted on the internet in various job portals. But some of the jobs being posted online are actually fake jobs which lead to a theft of personal information and vital information. Thus, these fake jobs can be precisely detected and classified from a pool of job posts of both fake and real jobs by using advanced deep learning as well as machine learning classification algorithms. In this paper, machine learning and deep learning algorithms are used so as to detect fake jobs and to differentiate them from real jobs. The data analysis part and data cleaning part are also proposed in this paper, so that the classification algorithm applied is highly precise and accurate. It has to be noted that the data cleaning step is a very important step in machine learning project because it actually determines the accuracy of the machine learning as well as deep learning algorithms. Hence a great importance is emphasized on data cleaning and pre-processing step in this paper. The classification and detection of fake jobs can be done with high accuracy and high precision. Hence the machine learning and deep learning algorithms have to be applied on cleaned and pre-processed data in order to achieve a better accuracy. Further, deep learning neural networks are used so as to achieve higher accuracy. Finally all these classification models are compared with each other to find the classification algorithm with highest accuracy and precision.

An Efficient Approach to Detect Fraudulent Service Enrollment Websites with Novel Random Forest and Compare the Accuracy with XGBoost Machine Algorithm

“…The real-time dataset that was used was the meteorological dataset. The input dataset for the proposed study is the meteorological dataset, which was downloaded as CSV files from kaggle.com (Anita et al 2021). "Fraud characteristics," "Business profile," "Required experience and education," "Fraudulent," "Functions," "Benefits," "Description," and "Company Logo" were the major parameters utilized to increase accuracy (%).…”

A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques

“…Several databases and other online resources were browsed for literature on related topics in order to better understand the techniques and data analysis used. The study of Anita et al (2021) used logistic regression (LR), k-nearest neighbor (k-NN), Random Forest (RF), and deep learning (DL) algorithms for detecting fraudulent jobs from a large pool of real data and found that DL performed best. Another study by Tabassum et al (2021) applied seven different ML algorithms and found the highest accuracy of two classifiers at 95.17%.…”

“…The organizations trap the young talent to defraud them of their money and personal information ( Ranparia, Kumari & Sahani, 2020 ). In this way, cybercriminals collect applicants’ information to resell or use later for their purposes ( Anita et al, 2021 ).…”