A Novel Fake Job Posting Detection: An Empirical Study and Performance Evaluation Using ML and Ensemble Techniques

Recently, everything can be accomplished online, including education, shopping, banking, etc. This technological advancement makes it easy for fraudsters to scam people online and acquire easy money. Numerous cyber crimes worldwide exist, including identity theft and fake job postings. Nowadays, many companies post job openings online, making recruitment simple. Consequently, fraudsters also post job openings online to obtain money and personal information from job seekers. In the proposed work, we aimed to decrease the frequency of such scams by using ensemble techniques such as AdaBoost, Gradient Boost, Stacking classifier, XgBoost, Bagging, and Random Forest to identify fake job postings from genuine ones. This paper proposes various featurization techniques such as Response coding with Laplace smoothing, Average Word2vec, and term frequency-inverse document frequency weighted Word2vec. We compared the performance of ensemble techniques with machine learning (ML) algorithms on publicly available EMSCAD dataset using accuracy and F1-score. Bagging classifier outperformed all the models with an accuracy of 98.85% and an F1-score of 0.88 on imbalanced dataset. On balanced dataset, XgBoost achieved 97.89% accuracy and 0.98 F1-score. From the experimental results, it is observed that a combination of ensemble and featurization techniques using Laplace smoothed Response coding and BoW stood superior to most of the state-of-the-art works on fake job posting detection.