Detection of Fake Job Listings Using Text Classification and SMOTE-Enhanced Training

Abstract: Online job portals are widely used for finding employment, but they are also exploited by scammers who create fake job postings to deceive job seekers. These fraudulent postings often appear legitimate and can lead to identity theft, financial loss, and misuse of personal information. This work proposes a machine learning-based approach to automatically detect fake job posts by analyzing textual and descriptive features from job advertisements. The dataset used in this study is sourced from Kaggle and consists of both real and fake job listings. The text data is preprocessed and transformed into numerical form using TF- IDF, and class imbalance is handled using SMOTE. Several machine learning models including Logistic Regression, Random Forest, and XGBoost were trained and evaluated. Among these, the XGBoost model achieved the highest performance with an accuracy of approximately 97.5%, demonstrating its effectiveness in identifying fraudulent job postings. This system can assist job platforms and users in improving trust and safety by filtering out scam job posts automatically.

Keywords: Fake Job Posts, Machine Learning, XGBoost, TF- IDF, SMOTE, Online Recruitment Fraud Detection.