Providing a Three-Dimensional Tensor Approach For Classifying and Detecting Fake News - A Case Study of Persian Newsin The Field of COVID-19

Convolutional neural networks, as one of the most important models of deep learning, have gained high accuracy on these issues. In this study, discussion and text analysis at the sentence level and improving the performance of neural networks to detect fake news has been convolution. The network of words for bags of words in the data model so that each word according to the two-dimensional vector space to become matrices. One of the limitations of convolutional networks is that it works at the word level and cannot consider the relationship and distance between sentences. And sentence-level analysis is a major problem in this research. Sentence level analysis is a major problem in this research.In this research, a basic model based on convolutional networks is proposed in which documents are given to the network in the form of 3D tensors to solve the mentioned problem. Considering 3D tensors allows the model to learn the position of words in a sentence and achieve more accurate results in detecting fake news.

This study is applied research in which about 42,000 Persian news from different cities of Iran were collected from Twitter and using preprocessing, additional and useless data is deleted and after tagging the deleted texts, the news text is used for the proposed approach using Python software and related libraries.

During testing, some machine learning algorithms had more power in classification problems, but with the changes in the structure of the convolutional network algorithm, better results were obtained than machine learning algorithms and other similar algorithms.

Considering 3D tensors allows the model to learn the position of words in a sentence, and this proposed model has gained considerable accuracy compared to the proposed approaches in the literature. The proposed model without adding additional overhead in terms of the number of features and network depth, by changing the input has been able to achieve better and more acceptable results than other approaches in the literature and achieve an accuracy of more than 94%.