International Journal For Multidisciplinary Research

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Fake Job Post Detection using Machine Learning

Author(s) Maddi Sravya Reddy, Maddikera Hemanth Lal, Lingam Sainad, Sandeep Agarwalla
Country India
Abstract The proliferation of online job platforms has given rise to a concerning increase in fraudulent job postings, presenting significant risks to job seekers and undermining the credibility of the job market. This research paper aims to address the pressing issue of fake job post identification by leveraging machine learning techniques. The primary objective is to develop a robust automated tool capable of accurately distinguishing between authentic and deceptive job advertisements. The proposed methodology utilizes a range of machine learning algorithms, incorporating supervised learning techniques and natural language processing methods, to analyze and classify job postings. Through the integration of both single classifiers and ensemble classifiers, the system evaluates and compares results, effectively detecting fraudulent job postings on the web. The study underscores the need for a proactive approach, acknowledging the dynamic tactics employed by scammers. Continuous refinement and adaptation of the machine learning models are emphasized to stay ahead of evolving fraudulent strategies. Ultimately, this research contributes to establishing a more secure online job market, fostering trust among job seekers and mitigating the financial and emotional risks associated with deceptive job postings.
Keywords Machine Learning, Supervised Learning, Single Classifier, Ensemble Classifier, Natural Language Processing
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-24
Cite This Fake Job Post Detection using Machine Learning - Maddi Sravya Reddy, Maddikera Hemanth Lal, Lingam Sainad, Sandeep Agarwalla - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.13906
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.13906
Short DOI https://doi.org/gtjttz
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