An Approach to Identify Epidemic Diseases Rumors in Social Networks ‎using Deep Learning Techniques

Document Type : Persian Original Article

Authors

1 Department of Computer Engineering, Ilam Branch, Islamic Azad University, Ilam, Iran.

2 Department of Computer Engineering, Abadan Branch, Islamic Azad University, Abadan, Iran.

Abstract

One of the most important issues in social networks is the high volume of rumors that are spread by human or machine ‎agents. In such situations, automatic detection of rumors to keep public opinion safe from their potential dangers is of ‎great importance. In this research, using deep learning techniques, a new solution for automatically detecting rumors ‎related to epidemic diseases in social networks will be presented. In the proposed method, first the content of existing ‎messages is prepared for processing in the next steps. Also, weight matrix format has been used to describe content ‎characteristics. Then, in the second step of the proposed method, the convolutional neural network is used to extract the ‎set of suitable features from the matrix of features obtained from the previous step. In this way, the matrix of content ‎features is used as the input of the deep neural network, and the weight values obtained in the last fully connected layer of ‎this neural network are used as the features extracted from it. Finally, the aggregation of several binary classifiers is used ‎in order to detect rumors and classify the features extracted through convolutional neural network. For this purpose, the ‎extracted features are simultaneously processed by several learning models and the final output of the proposed system is ‎determined by voting the outputs of these three algorithms. The results of this research show that by using the proposed ‎method, rumors can be detected with an average accuracy of 98.8%, which shows an improvement of at least 2.4% in ‎detection accuracy compared to the previous methods.‎

Keywords

References

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Journal of Soft Computing and Information Technology
Volume 12, Issue 3
December 2023
Pages 30-44

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