Fake Job Recruitment Detection Using Machine Learning Approach

The increasing number of fake social media posts together with fraudulent content has become a major factor in online fraud growth which causes people to doubt trustworthiness and security. The authentication of post authenticity has evolved into a vital operation because user-generated content increases dynamically every day. The research investigates the effectiveness of XGBoost and Random Forest as well as Logistic Regression for classifying posts into real or fake categories. A total of 18,000 different online scam-related posts comprised the Employment Scam Aegean Dataset (EMSCAD). These algorithms show high success rates in detecting genuine content because they use their gained knowledge from previous data analysis. The study delivers important findings to automate scam detection systems which lead to better security measures and lower online fraudulent risks on different platforms. Keywords: data mining techniques, classification algorithms, XGBoost, Catboost, Light Gradient Boosting, Random Forest, Logistic Regression.