Detecting Fake Job Postings: A Hybrid NLP an...

Detecting Fake Job Postings: A Hybrid NLP and Random Forest Approach

Author Name : Bilal Akhtar, Harsh Surana, Shreyan Awasthi, Sanchita Chourawar

ABSTRACT With the increasing digitalization of recruitment platforms, job seekers are exposed to a growing number of fraudulent job postings. This research presents a robust hybrid framework that combines Natural Language Processing (NLP) and Random Forest machine learning techniques to detect and prevent fake job postings. Utilizing a dataset of 25,000 job listings collected from various platforms, including Naukri, LinkedIn, and Telegram, the model identifies scams with 93.4% accuracy and a prediction latency of just 89 milliseconds. Our approach integrates TFIDF, Word2Vec embeddings, and 47 engineered linguistic and metadata features. Emphasis is given to regional scam patterns within India, offering specific recommendations for deployment and policy action.