The Growing Threat of Fake Job Postings: A Review of Machine Learning and NLP-Based Detection Approaches
DOI:
https://doi.org/10.1807/6rw1fg49Keywords:
Fake Job, Detection, Threat, Cyber Fraud, Machine Learning, Natural Language Processing, Deep LearningAbstract
This review paper discusses the Fake Job Post Detection, spotlighting machine-learning, deep-learning, and natural-language-processing tools built to shield young job seekers from con artists and keep the hiring scene honest. To ground the discussion, an in-depth survey of past studies maps the techniques and trends that now dominate the field. The main contribution is a side-by-side comparison of existing systems, spelling out where each shines and where it falls short. The paper goes on to summarize the key hurdles and opportunities investigators still face. A pressing call for larger, cleaner datasets, closer industry partnerships, and fraud alerts that fire in real time runs through the review. By weaving these threads together, the article offers a fresh perspective on what is known today and sketches concrete directions for future studies and policy action in the fast-changing area of fake-job-ad detection.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License.
