Fake Job Recruitment Detection Using Machine Learning

The increasing number of fake social media posts together with fraudulent content has become a major factor in online fraud growth which causes people to doubt trustworthiness and security. The authentication of post authenticity has evolved into a vital operation because user-generated content increases dynamically every day. The research investigates the effectiveness of XGBoost and Random Forest as well as Logistic Regression for classifying posts into real or fake categories. A total of 18,000 different online scam-related posts comprised the Employment Scam Aegean Dataset (EMSCAD). These algorithms show high success rates in detecting genuine content because they use their gained knowledge from previous data analysis. The study delivers important findings to automate scam detection systems which lead to better security measures and lower online fraudulent risks on different platforms.

  • Research Type: Policy Research
  • Paper Type: Report Paper
  • Vol.7 , Issue 5 , Pages: 25 - 30, Oct 2025
  • Published on: 07 Oct, 2025
  • Issue Type: Regular
  • Cite Score
    :

    100

  • No. of authors
    :

    75

  • No. of Downloads
    :

    43

  • Cite Score
    :

    100

  • No. of authors
    :

    75

  • No. of Downloads
    :

    43

  • Cite Score
    :

    100

  • No. of authors
    :

    75

  • No. of Downloads
    :

    43

About Authors:
Vijaya Bhaskar Reddy
India
Viswam Engineering College

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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Corresponding Author: Vijaya Bhaskar Reddy, bvijay.br@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Edited by:
  • Editor-In-Chief
    IJRDES
Reviewed by:
  • Akshay C
    Akshay C
    India
    Viswam Engineering College
  • C.Manoj kumar
    C.Manoj kumar
    India
    Aditya College of Engineering
  • K.Dinesh
    K.Dinesh
    India
    Aditya College of Engineering
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