Fake Job Posting Detection Using LSTM-Based Deep Learning Model
Khushi Yadav
Scholar, Department of Computer Science & Engineering, (AI&ML), KIPM College of Engineering and Technology, U.P., India
Prince Kannaujiya
Scholar, Department of Computer Science & Engineering, (AI&ML), KIPM College of Engineering and Technology, U.P., India
Annapurna Pandey
Scholar, Department of Computer Science & Engineering, (AI&ML), KIPM College of Engineering and Technology, U.P., India
Akarsh Yadav
Assistant Professor, Department of Computer Science and Engineering, (AI&ML), KIPM College of Engineering and Technology, U.P., India
📌 DOI: https://doi.org/10.63920/tjths.52031
🔑 Keywords: Fake Job Posting Detection, LSTM, NLP, Deep Learning, Fraud Detection, Text Classification
📅 Publication Date: 26 April 2026
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Abstract:
With the rapid growth of online recruitment platforms, fake job postings have become a major concern, leading to financial loss and misuse of personal information of job seekers. This paper presents an intelligent approach for detecting fraudulent job postings using a Long Short-Term Memory (LSTM) based deep learning model combined with Natural Language Processing (NLP) techniques. The proposed system analyzes textual features such as job descriptions, requirements, and company profiles to classify postings as genuine or fake. Various preprocessing techniques including tokenization, stopword removal, and text normalization are applied to enhance the quality of input data. The LSTM model effectively captures sequential dependencies in textual data, improving the model’s ability to detect hidden patterns in fraudulent content. The performance of the model is evaluated using standard metrics such as accuracy and precision, showing promising results. The proposed approach can assist job portals and users in identifying suspicious listings and reducing the risk of online recruitment fraud.
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📖 How to Cite
Khushi Y., Prince K., Annapurna P., Akarsh Y. (2026). Fake Job Posting Detection Using LSTM-Based Deep Learning Model. TEJAS J. Technol. Humanit. Sci.,, Vol. 05, Issue 02. https://doi.org/10.63920/tjths.52031
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References
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