Abstract
Fake news can cause not only individual but also national damages. In this study, we aim to select artificial intelligence techniques for identifying fake news and compare and analyze these techniques based on the results obtained by applying them to the news dataset. To achieve this, we analyzed the characteristics of the news dataset and implemented models for discriminating fake news using six artificial intelligence techniques: logistic regression, naive bayes, random forest, support vector machine, perceptron, and recurrent neural network. Finally, through analysis of the experimental results, we investigated the importance of various factors such as sentence length, vocabulary size, hyper parameters of the artificial intelligence models, and the ratio of training data to test data in influencing the performance of the models after natural language processing during training.
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