The Use of LSTM Neural Network to Detect Fake News on Persian Twitter - et. al., Mohammad Mohsen Sadr, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The spread of new Internet-based technologies has caused various news and events to be widely disseminated on various networks and quickly made available to the public. This has diminished the boundaries between news production and information creation and sharing in online media and the social media environment. The spread of fake news is a serious problem that has been created by the open nature of the web and social media, resulting in an increasing trend of fake news creation and publication. Meanwhile, the volume of fake news produced is very large and is adjusted in a way that easily deceives the audience. Machine learning and neural networks algorithms are commonly used to prevent the spread of fake news. In this study, a hybrid model of long short term memory (LSTM) and 14-layer bidirectional long short term memory (BLSTM) neural network has been used to identify the fake news on Persian texts and tweets. Based on the obtained results, the proposed model has the ability to identify fake news and rumors with 91.08% accuracy. According to the confusion matrix, it has been determined that the performance capability of the proposed model is 92.05%, its recall is 91.10%, and its f1 criterion is 91.57%. We further compare the results to Bayesian, k-NN, random forest, linear regression, perceptron neural network, SVM, decision tree, probabilistic gradient, Adaboost, gradient boost, and extra tree. The results show that the proposed approach outperforms the other algorithms.