Authors : Abirami K.; Jhonsi M.; Vaishnavi S.; Lakshmi M.

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/43cu35wd

Scribd : https://tinyurl.com/rpjj4kvb

DOI : https://doi.org/10.38124/ijisrt/26May786

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid expansion of online job portals, social media platforms, and messaging applications has made it easier for college students to access internship and job opportunities. However, this convenience has also led to a significant rise in fraudulent offers targeting students. These fake opportunities often attract candidates by promising high salaries, guaranteed placements, or easy hiring processes, while secretly aiming to collect registration fees or sensitive personal information.Due to limited awareness and the absence of effective verification mechanisms, many students fall victim to such scams. Traditional methods of verifying job authenticity are often manual, time-consuming, and unreliable. To address this issue, this study proposes an AI-based detection system that leverages Natural Language Processing (NLP) techniques to analyze the textual content of job and internship offers.The proposed system aims to automatically classify opportunities as genuine or fraudulent based on linguistic patterns, keywords, and contextual analysis. By implementing machine learning models trained on real-world datasets, this approach enhances accuracy and reduces human effort in identifying scams. Ultimately, this system seeks to provide a reliable and efficient solution to safeguard students from deceptive job offers and support them in making informed career decisions.

Keywords : Fake Job Detection, NLP, Machine Learning, Text Classification, Fraud Detection, Online Recruitment Scams, Artificial Intelligence, Student Safety.

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