Вирішення проблеми публікації фейкових вакансій в інтернеті

Abstract

Fake job postings pose a significant threat to job seekers, eroding trust in the online recruitment environment. This paper outlines a comprehensive strategy that integrates automated text analysis, manual moderation, and robust legal frameworks to identify and remove fraudulent vacancies. The methodology leverages machine learning algorithms to detect anomalies in job postings, while user feedback mechanisms help continuously refine the detection process. Additionally, educational initiatives aim to equip candidates with the knowledge to recognize and avoid suspicious offers, thereby creating a safer and more transparent job market. Building on these approaches, the proposed model emphasizes multilevel verification and prompt incident reporting to bolster system integrity and user confidence.