Machine Learning Algorithm for Fake Job Detection Systems

Abstract: The fake news on social media and various other media is wide spreading and is a matter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. To avoid fraudulent post for job in the internet, an automated tool using machine learning based classification techniques is proposed. Different classifiers are used for checking fraudulent post in the web and the results of those classifiers are compared for identifying the best employment scam detection model. It helps in detecting fake job posts from an enormous number of posts. Two major types of classifiers, such as single classifier and ensemble classifiers are considered for fraudulent job posts detection. However, experimental results indicate that ensemble classifiers are the best classification to detect scams over the single classifiers. This Paper makes an analysis of the research related to fake news detection and explores the traditional machine learning models to choose the best, in order to create a model of a product with supervised machine learning like random forest algorithm, that can classify fake news as true or false, by using tools like python Scikit-learn. This process will result in feature extraction and vectorization; we propose using Python Scikit-learn library to perform tokenization and feature extraction of text data, because this library contains useful tools like Count Vectorized and Tiff Vectorized.

Keywords: Fake news, random forest algorithm, ensemble classifier, Accuracy, feature extraction Cite: Dr.P.Manikandaprabhu, Loganisha S, "Machine Learning Algorithm for Fake Job Detection Systems", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.133147.