Ye, N.; Yu, D.; Zhou, Y.; Shang, K.-K.; Zhang, S. Graph Convolutional-Based Deep Residual Modeling for Rumor Detection on Social Media. Mathematics2023, 11, 3393.
Ye, N.; Yu, D.; Zhou, Y.; Shang, K.-K.; Zhang, S. Graph Convolutional-Based Deep Residual Modeling for Rumor Detection on Social Media. Mathematics 2023, 11, 3393.
Ye, N.; Yu, D.; Zhou, Y.; Shang, K.-K.; Zhang, S. Graph Convolutional-Based Deep Residual Modeling for Rumor Detection on Social Media. Mathematics2023, 11, 3393.
Ye, N.; Yu, D.; Zhou, Y.; Shang, K.-K.; Zhang, S. Graph Convolutional-Based Deep Residual Modeling for Rumor Detection on Social Media. Mathematics 2023, 11, 3393.
Abstract
The popularity and development of social media has made it more and more convenient to
spread rumors, and it has become especially important to detect rumor information from massive
amounts of information. Most of the traditional rumor detection methods use content characteristics or propagation structure to mine rumor characteristics, ignoring the fusion characteristics of content and structure and their interaction characteristics. Therefore, a novel rumor detection method based on heterogeneous convolutional networks is proposed. Firstly, this paper constructs a heterogeneous map of joint rumor content and propagation structure to explore the interaction between content and propagation structure during rumor propagation and obtain rumor representation. On that basis, this paper uses the deep residual graph convolutional neural network to construct the content and structure interaction information of the current network propagation model. Finally, this paper uses the two datasets of Twitter15 and Twitter16 to verify the proposed method. Experimental results show that the proposed method has higher detection accuracy than the traditional rumor detection method.
Keywords
False information detection; Residual structure; Graph neural network
Subject
Social Sciences, Behavior Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
