A Comparative Study on Fake Job Post Prediction using Different Data Mining Techniques

Author(s)	N. Radhika, S. Tharun, H. Sujatha, D. Shekshavali
Country	India
Abstract	Nowadays, job advertisements are widely shared on the internet and social media platforms. Along with genuine job postings, the number of fake job posts is also increasing, which has become a serious problem for job seekers. Identifying whether a job post is real or fake is an important and challenging task. In this project, machine learning techniques are used to predict fraudulent job postings. A Random Forest classifier is applied for the prediction process. The Employment Scam Aegean Dataset (EMSCAD), which contains about 18,000 job records, is used for training and testing the model. The proposed system analyzes job details and classifies them as real or fraudulent. Experimental results show that the system can effectively detect fake job postings with approximately 98% accuracy.
Keywords	Fake job post detection, Data mining, machine learning, employment scam, text classification, EMSCAD dataset.
Discipline	Engineering
Published In	Volume 4, Issue 2, March-April 2026
Published On	2026-04-04

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Advanced International Journal of Multidisciplinary Research