International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Advanced Fake Job Post Prediction using Machine Learning for Online Recruitment Scam Detection |
|---|---|
| ABSTRACT | The exponential growth and universal popularity of online recruitment platforms have fundamentally revolutionized the modern hiring landscape, seamlessly connecting global employers with prospective job seekers. However, the unchecked proliferation of these digital platforms has inadvertently catalyzed a significant and alarming rise in fraudulent job advertisements. These sophisticated scams are meticulously designed to deceive desperate or unsuspecting applicants, ultimately aiming to maliciously harvest highly sensitive personal data, financial information, or direct monetary deposits. Traditional manual verification methodologies, reliant on human moderation, are fundamentally insufficient to handle the sheer velocity, variety, and volume of online job postings generated daily. This exhaustive research paper proposes an advanced, highly scalable machine learning framework explicitly engineered for detecting fake job advertisements. By extensively analyzing complex textual, contextual, and organizational features autonomously extracted from raw recruitment data, the proposed system leverages advanced Natural Language Processing (NLP) techniques for rigorous feature extraction. The study meticulously evaluates a diverse array of machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), Random Forest, XGBoost, and sophisticated voting ensemble classifiers. Experimental results comprehensively demonstrate that the proposed model effectively and consistently identifies fraudulent job postings, achieving exceptional metrics across accuracy, precision, recall, and the F1-score. Ultimately, this study significantly contributes to fortifying recruitment cybersecurity, preserving digital platform integrity, and critically protecting vulnerable job seekers from devastating online employment scams. |
| AUTHOR | K NAVYA SREE, DR.D. SURESH REDDY PG Student, Dept. of CSE, Siddartha Educational Academy Group of Institutions, Tirupati, India Assoc. Professor, Dept. of CSE(AI&ML), Siddartha Educational Academy Group of Institutions, Tirupati, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406023 |
| pdf/23_Advanced Fake Job Post Prediction using Machine Learning for Online Recruitment Scam Detection.pdf | |
| KEYWORDS | |
| References | 1. Vidros, S., Kolias, C., Kambourakis, G., & Akoglu, L. (2017). Automatic Detection of Online Recruitment Frauds. IEEE Transactions on Information Forensics and Security. 2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer Information Science and Statistics. 3. Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers. 4. Aggarwal, C. C. (2018). Machine Learning for Text. Springer International Publishing. 5. Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. |
