Research on Semi-Supervised Text Classification Based on Graph Attention Network

Liu, Li; Sun, Yongyong; Xu, Fei

doi:10.1109/iccnea57056.2022.00050

Cited by 1 publication

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“…However, these models need a lot of time to manually extract features. [27], improving the training speed of the model [28], and resolving data incompleteness issue [29,30]. Some scholars have also applied the GCN model to rumor detection.…”

Section: Related Workmentioning

confidence: 99%

Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining

Chen,

Li,

Yang

2023

Advances in Multimedia

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This paper addresses the problem of detecting internet rumors in social media. Rumors do great harm to information society, making rumor detection necessary. However, existing methods for detecting rumors generally only learn pattern features or text content features from the whole propagation process, which fall short in capturing multilevel features with topic relevance of text content from social media data. In this paper, we propose a novel graph convolution network model, named multilevel feature fusion-based graph convolution network (MFF-GCN) which can employ multiple streams of GCNs to learn different level features of rumor data, respectively. We build a heterogeneous tweet graph for each single-level feature GCN to encode the topic relation among tweets based on the text contents. Experiments on real-world Twitter data demonstrate that our proposed approach achieves much better performance than the state-of-the-art methods with higher values of precision and recall as well as their corresponding F1 score. In addition, the diversity of our experimental results shows the generalization ability of our model.

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