Due to recent advancements in social media and modern technologies, posting job openings online has become more prevalent. As a result, predicting deceptive job postings will be very important for everyone. Predicting fraudulent job listings encounters several obstacles similar to other classification tasks. This study suggests a machine learning method for identifying fraudulent job listings through a mix of text and category data. We gather different characteristics from the job listing, text, including the existence of specific keywords, along with elements from the job advertisement, such as the job position, type of employment, and necessary experience. Models such as Random Forest and Deep Neural Network are evaluated using different measures like accuracy, F1 score, ROC AUC score, and others following training. This study can be utilized to develop automated systems for identifying fraudulent job postings. Posts, aiding in safeguarding job seekers against scams and fraudulent actions in the employment market.
Maharajpet et al. (Tue,) studied this question.