Detection of Fake Job Descriptions Using NLP – An Intelligent NLP Framework for Identifying Fraudulent Recruitment Posts
Rahul Mishra
Department of Computer Science, Shri Ramswaroop Memorial University, Lucknow, India
Jasika Awasthi
Department of Computer Science, Shri Ramswaroop Memorial University, Lucknow, India
Mansi Yadav
Department of Computer Science, Shri Ramswaroop Memorial University, Lucknow, India
Homa Rizvi
Assistant Professor, Department of Computer Science, Shri Ramswaroop Memorial University, Lucknow, India
📌 DOI: https://doi.org/10.63920/tjths.52014
🔑 Keywords: Fake Job Detection, Natural Language Processing, Machine Learning, TF-IDF, Recruitment Fraud
📅 Publication Date: 11 April 2026
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Abstract:
This paper proposes an intelligent Natural Language Processing (NLP) based framework for detecting fraudulent job descriptions posted on online recruitment platforms. The system applies preprocessing techniques including tokenization, stop word removal, stemming, and lemmatization. Feature extraction methods such as Bag of Words and TF-IDF are used to convert textual job descriptions into numerical form. Multiple machine learning models including Logistic Regression, Naïve Bayes, Random Forest, and Support Vector Machine are trained to classify job postings as genuine or fake.
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📖 How to Cite
Rahul M., Jasika A., Mansi Y., and Homa R. (2026). Detection of Fake Job Descriptions Using NLP – An Intelligent NLP Framework for Identifying Fraudulent Recruitment Posts. TEJAS J. Technol. Humanit. Sci.,, Vol. 05, Issue 02. https://doi.org/10.63920/tjths.52014
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References
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