Performance comparison of two class boosted decision tree snd two class decision forest algorithms in predicting fake job postings

Abstract

During the Corona Virus Disease 2019 (COVID 19) period, online activities have become a necessary thing in everyone's life. However,in electronic recruitment, fake job postings have been started by scammers to get people's personal information and scam purposes. Many businesses prefer to post their vacancies electronically so that job applicants can access them quickly and timely. But this purpose may be one form of scam on the part of the fraud individuals because they give job applicants during terms of taking money from them or collecting their personal information for involving in cybercrimes. Fake job posting advertisements can be written against a reputable firm for breaching its reputation. The fraudulent post-detection work draws proper attention to obtaining an automated tool to identify fake jobs and report them to people to avoid applying for such situations. At present, many machine learning algorithms have been used to detect such fraudulent posts. But, the performance of such algorithms to be measured and compared to find a proper algorithm to incorporate in identifying fake things. In this research, the use of a proposed model with the help of Microsoft Azure Machine Learning Studio tested a comparison study on the performance of a two - class boosted decision tree and two - class decision forest algorithms. Researchers used F1 Score, Recall. Accuracy and precision to compare those two algorithms. Results showed that a two - class boosted decision tree is better for detecting fake job posts than the two - class forest decision algorithm. Thus, a two - class decision forest algorithm can be used to find and identify false or gossip messages, tweets, and social media publications.