Contact : 9561854529 Email : submitpaper@ijaea.com editorinchief.ijaea@gmail.com Working Hours: 9am to 9PM

International Journal of Advanced Engineering Application

ISSN: 3048-6807

Submit Paper Apply for Reviewer

Smart Detection of Fake Job Post Using Albert + Xgboost Intelligence

Author(s):Mrs. Siriginedi Sai Brundavanam1, Malleswari Gujjula2, Raavi Samba Siva Reddy3, Yarra Harika4, Dhupati Srinivasu5

Affiliation: Department of Computer Science and Engineering, Dhanekula Institute of Engineering and Technology, Ganguru, AP, India

Page No: 45-50

Volume issue & Publishing Year: Volume 2 Issue 4,April-2025

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI: https://doi.org/10.5281/zenodo.17659358

Download PDF

Article Indexing:

Abstract:
The increase of false job adverts across the internet has resulted in a spike in employment fraud which greatly endangers a job seeker’s finances as well as their identity. Many people fail to identify which job listings are authentic and which ones are fake, making them an easy target for fraudsters. This project proposes a solution in the form of a Fake Job Post Detection System which applies ALBERT for the extraction of deep contextual features from the job descriptions and XGBoost for categorizing the posts into real or fake. It is trained on a Kaggle dataset containing labeled job postings with details such as job title, company name, and description. The system takes user-inputted job details and identifies patterns associated with fake job listings. ALBERT extracts linguistic features and XGBoost makes predictions based on the extracted features. To build trust Explainable AI (XAI) is incorporated wherein the user can comprehend the decision-making process. This system not only assists job seekers in spotting fraudulent job advertisements but also encourages a safer job-seeking experience.

Keywords: Fake job post detection, ALBERT, XGBoost, Hybrid architecture, Deep learning, Natural language processing (NLP).

Reference:

  • [1]. IEEE Xplore, ”Detection of Fake Online Recruitment Using Machine Learning Techniques”, https://ieeexplore.ieee.org/document/10074276
  • [2]. SPRINGER, “A machine learning approach to detecting fraudulent job types”, https://link.springer.com/article/10.1007/s00146-022-01469-0?utm_source=chatgpt.com
  • [3]. IJSET, “Fake Job Post Detection Website”,
  • https://www.ijset.in/wp-content/uploads/IJSET_V12_issue5_799.pdf
  • [4]. IEEE Xplore, ”Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches”, https://ieeexplore.ieee.org/document/10614582
  • [5]. IEEE Xpert, ”Fake Job Post Detection using Machine Learning”,
  • https://www.ieeexpert.com/python-projects/fake-job-post-detection-using-machine-learning/
  • [6]. ResearchGate, “ Fake Job Detection & Analysis Using Machine Learning & Deep Learning Algorithms”, https://www.researchgate.net/publication/352159024_Fake_Job_Detection_and_Analysis_Using_Machine_Learning_and_Deep_Learning_Algorithms
  • [7]. IJFMR, ”Fake Job Post Detection using Machine Learning”,
  • https://www.ijfmr.com/papers/2024/1/13906.pdf
  • [8]. ACM Digital Library, “Detection of Fake Job Postings by Utilizing Machine Learning and Natural Language Processing Approaches”,https://dl.acm.org/doi/10.1007/s11063-021-10727-z\
  • [9]. ResearchGate, “A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques”,
  • https://www.researchgate.net/publication/349884280_A_Comparative_Study_on_Fake_Job_Post_Prediction_Using_Different_Data_mining_Techniques
  • [10]. JETIR, “Prediction of Fake Job Ad using NLP-based Multilayer Perceptron”,
  • https://www.jetir.org/papers/JETIR2404340.pdf
  • [11]. SPRINGER, “Fraud-BERT: transformer based context aware online recruitment fraud detection”,
  • https://link.springer.com/article/10.1007/s10791-025-09502