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@article{202032,
        author = {Karnan S and Dhinesh Ragavendar R and Anand R and Praveenkumar V},
        title = {Detection of fake job advertisement using machine learning techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {9883-9890},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202032},
        abstract = {The increasing number of job posting websites has created many opportunities for people seeking employment, but it has also contributed to the creation of many fake job listings. These fake job postings have been known to dupe jobseekers into losing money or compromising their personal information. Current methods of detecting fake job posts involve manual verification processes, which are slow, inefficient, and subjective. This study attempts to solve this problem by proposing a machine learning-based fake job detection framework using the algorithmic technique of Logistic Regression. The model utilizes a structured approach, beginning with the pre-processing of the data followed by the feature extraction of each job post with the aid of TF-IDF vectorization and finally ending with the classification of each instance as either genuine or fake. Several models were studied, including Random Forest, LightGBM, and XGBoost, but based on the characteristics and requirements of this model, Logistic Regression was found to be the best fit.},
        keywords = {},
        month = {May},
        }