A decision-making system for detecting fake persian news by improving deep learning algorithms– case study of Covid-۱۹ news
سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 198
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شناسه ملی سند علمی:
JR_APRIE-8-0_013
تاریخ نمایه سازی: 17 اسفند 1400
چکیده مقاله:
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.
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نویسندگان
Vahid Mottaghi
Department of IT Management, Qeshm Branch, Islamic Azad University, Qeshm, Iran.
Mahdi Esmaeili
Department of Computer Science, Kashan Branch, Islamic Azad University, Kashan, Iran.
Ghasem Ali Bazaee
Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Mohammadali Afshar Kazemi
Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
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