Improved Job Scam Detection Methods Using Machine Learning and Resampling Techniques

in recent years, job scams, especially online job scams, have become one of the most serious social problems worldwide and have brought great threats to the safety of people's lives and property. This study aimed to develop a machine learning predictive model for the authenticity of job postings to help people identify job scams and get access to real job opportunities. In this study, we considered the significant class imbalance in the dataset and used the TF-IDF method to extract text features from the text fields. The performance of various machine learning models was evaluated using balanced data generated through sampling techniques. The results show that the random forest model performs exceptionally well in handling balanced data and maintaining better accuracy (AC) and recall (RC) for both regular and generated balanced data. It also proved that TF-IDF is an effective method to extract features from text data.