A Comparative Analysis of Fraudulent Recruitment Advertisement Detection Methods in the IoT Environment

Abstract

The growth of the Internet of Things has changed the way of job hunting. Online recruitment has gradually replaced the traditional offline recruitment mode. Some unscrupulous people use online recruitment platforms to publish fraudulent recruitment advertisements, which not only bring financial and reputational losses to job seekers but also harm the sustainable development of society. However, previous studies have not used unified evaluation metrics and datasets, and detecting fraudulent recruitment advertisements lacks systematic research. To resolve this problem, this paper selects four representative traditional learning methods (i.e., random forest, support vector machine (SVM), logistic regression, and Naïve Bayes) and three deep learning methods (i.e., TextCNN, gate recurrent unit (GRU), and bidirectional long-short-term memory (Bi-LSTM)), which perform good in natural language processing (NLP) and use the same evaluation metrics and datasets conducting comparative experiments on balanced and unbalanced datasets, respectively. The experimental results show that the TextCNN method achieves the best detection performance with relatively low energy consumption on the balanced dataset. All the metrics values are more significant than 0.93. On unbalanced datasets, the TextCNN method still performs best with increasing imbalanced proportion.