A decision-making system for detecting fake persian news by improving deep learning algorithms– case study of Covid-۱۹ news

With the increase of news on social networks, a way to identify fake news has become an essential matter. Classification is a fundamental task in natural language processing (NLP). Convolutional neural network (CNN), as a popular deep learning model, has shown remarkable success in the task of fake news classification. In this paper, new baseline models were studied for fake news classification using CNN. In these models, documents are fed to the network as a ۳-dimensional tensor representation to provide sentence-level analysis. Applying such a method enables the models to take advantage of the positional information of the sentences in the texts. Besides, analyzing adjacent sentences allows extracting additional features. The proposed models were compared with the state-of-the-art models using a collection of real and fake news extracted from Twitter about covid-۱۹, and the fusion layer was used as the decision layer in selecting the best feature. The results showed that the proposed models had better performance, particularly in these documents, and the results were obtained with ۹۷.۳۳% accuracy for classification on Covid-۱۹ after reviewing the evaluation criteria of the proposed decision system model.

کلیدواژه ها:

Fake News ، Text Classification ، Decision-making ، Deep Learning ، convolutional neural network ، Natural Language Processing