A Guided Derivative Topic Dissemination Model Based on Topic Identity and Transfer Learning

In topic dissemination, accurately predicting the behavior of derived topic dissemination models enables the implementation of timely measures to effectively mitigate negative impacts. Addressing the issues of data scarcity and the influence of leaders on users, this article proposes a guided derived topic propagation model based on topic identity and transfer learning. To tackle the sparsity of early data on derived topics, we introduce generative adversarial networks (GANs) to enhance data integrity and continuity, given their efficacy in compensating for missing data. Additionally, recognizing the potential for domain shifts during topic evolution, we incorporate transfer learning to facilitate domain adaptation and improve the accuracy of the derived topic propagation model. To address the significant impact of leader authority on user perspectives, we incorporate the social psychological concept of “identity theory” and design a metric for topic identity based on this theory to quantify the leader’s influence on user viewpoints. Concurrently, considering user identification with topics, we propose a metric for topic domain co-presence, incorporating interest preferences to more accurately represent user engagement with the topic. Finally, to account for the dynamic nature of user viewpoints, we employ graph convolutional networks (GCNs) to model user perspectives over time. The use of GCNs, based on temporal slices, effectively handles the interconnections between user nodes and explores the influence of interuser interactions on viewpoints. Experimental results demonstrate that the proposed model not only enhances data integrity and continuity but also improves the prediction of the propagation dynamics of guided derived topics.