Detection Of Fake And Irrelevant Job Postings Using Passive Aggressive Classifier

• Author(s):

  Prof Sasikala S | AbinayaShri S | Dhivya Dharshini R | Sathya D | Vaishnavi R

• Keywords:

  Fake Job Detection, Passive Aggressive Classifier, TF-IDF, NLP, Machine Learning, Job Portal, Fraud Detection.

• Abstract:

  With The Rapid Growth Of Online Job Portals, The Number Of Fake And Irrelevant Job Postings Has Significantly Increased. These Fraudulent Listings Mislead Job Seekers, Waste Time, And Sometimes Lead To Financial Loss. This Paper Presents A Machine Learning–based Approach To Detect Fake And Irrelevant Job Postings Using A Passive Aggressive Classifier. A Dataset Of 10,000 Job Postings Collected From Kaggle Was Used For Training And Evaluation. Natural Language Processing (NLP) Techniques Such As TF-IDF Are Applied To Convert Textual Data Into Numerical Features. The Proposed System Is Integrated Into A Web Platform Named TrueHire, Which Provides Verified Job Listings. The Model Achieves An Accuracy Of 71.93%, Demonstrating Its Effectiveness In Identifying Fraudulent And Irrelevant Postings.

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Other Details

• Paper id:

  IJSARTV12I4105019

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-15

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