Fake Job Post Detection Using Machine Learning

Rama Lakshmi; Chaitanya Sarvani N; Meghana Ginjala; Madhappavar Nikhil

doi:10.36948/ijfmr.2026.v08i03.76950

Fake Job Post Detection Using Machine Learning

Author(s)	Rama Lakshmi, Chaitanya Sarvani N, Meghana Ginjala, Madhappavar Nikhil
Country	India
Abstract	With online job portals, job searching has become more convenient and accessible, with a tangible increase in fraudulent job posts. A significant part of these listings is well developed, to look authentic and users may struggle to differentiate between authentic opportunities and fraud. This work has created a machine learning-based system by detecting fake job postings through textual features of the job title, description, and requirements. TF-IDF vectoriza-tion processes the text data to convert it into numbers allowing the models to detect meaningful patterns in the job descriptions. Numerous machine learning models are trained and evaluated to identify the best approach in classification. The highest performance in the Support Vector Machine (SVM) model was recorded in the course of experimentation in terms of accuracy. The chosen model is implemented in a web application based on Flask, so the user can examine job approvals in real-time and get instant feedback. Besides prediction, the system also has some practical functions, including scam reporting, scam knowledge base, and one that checks the legitimacy of the company. These attributes make the system useful and offer users with more means of verifying job posts. The findings prove that the system is able to detect suspicious job posts efficiently without being complex and inaccessible to the average users.
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-02
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.76950

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International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Fake Job Post Detection Using Machine Learning

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