Fake job postings have become prevalent in the online job market, posing significant challenges to job seekers and employers. Despite the growing need to address this problem, there is limited research that leverages deep learning techniques for the detection of fraudulent job advertisements. This study aims to fill the gap by employing a Bidirectional Long Short-Term Memory (Bi-LSTM) model to identify fake job advertisements. Our approach considers both numeric and text features, effectively capturing the underlying patterns and relationships within the data. The proposed model demonstrates a superior performance, achieving a 0.91 ROC AUC score and a 98.71% accuracy rate, indicating its potential for practical applications in the online job market. The findings of this research contribute to the development of robust, automated tools that can help combat the proliferation of fake job postings and improve the overall integrity of the job search process. Moreover, we discuss challenges, future research directions, and ethical considerations related to our approach, aiming to inspire further exploration and development of practical solutions to combat online job fraud.
https://arxiv.org/abs/2304.02019
@article{2023, doi = {10.56726/irjmets35202}, url = {https://doi.org/10.56726%2Firjmets35202}, year = 2023, month = {apr}, publisher = {International Research Journal of Modernization in Engineering Technology and Science}, title = {{DETECTING} {FAKE} {JOB} {POSTINGS} {USING} {BIDIRECTIONAL} {LSTM}}, journal = {International Research Journal of Modernization in Engineering Technology and Science}}