Fake Job Post Prediction

Rathudi, Aishwarya Dinesh (2023) Fake Job Post Prediction. Masters thesis, Dublin, National College of Ireland.

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Abstract

Online job platforms have revolutionized the job search process in the modern digital age by providing a wide range employment option. The growing number of employment scams raises concerns about the authenticity of these platforms. Such fraudulent job postings take the advantage of job seekers, leading to money losses, identity theft, and lost opportunities for employment. Addressing this issue is crucial for both job seekers and platform owners to maintain trust and credibility in the online job market. This study's motivation lies in addressing the impact of these scams on job seekers trust and online platform credibility. The research aims to develop predictive models using advanced machine learning techniques to effectively identify and prevent fraudulent job listings. This study successfully employs advanced neural network architectures, Long Short-Term Memory (LSTM) and Bidirectional LSTM(BiLSTM), and natural language processing (NLP) technique like word2vec, to tackle the challenge of detecting fake job postings. The proposed models achieve remarkable accuracy rates of 97.18% for LSTM and 96.86% for BiLSTM by rigorous data preparation, feature engineering, and hyperparameter tuning. This work enhances the safety of online job hunting, promoting user confidence while protecting against potential scams, by utilizing cutting-edge machine learning models.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Siddig, Abubakr UNSPECIFIED
Uncontrolled Keywords:	Fake Job postings; Natural Language Processing (NLP); machine learning; job scams; LSTM; Bidirectional LSTM
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing H Social Sciences > HD Industries. Land use. Labor > Issues of Labour and Work > Job Seeking Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	28 Dec 2024 15:41
Last Modified:	28 Dec 2024 15:41
URI:	https://norma.ncirl.ie/id/eprint/7258

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