Detection of Fake News on Microblog Based on Graph Convolutional Network
Authors: 
Yun Jia Jin, Jun Liu, Long Hao ChaiAuthors Info & Claims
Yun Jia Jin, Jun Liu, Long Hao ChaiAuthors Info & ClaimsArticle No.: 30, Pages 1 - 6
Abstract
In the Internet era of information explosion, accurate automatic rumor identification is increasingly important. However, currently available microblog rumor identification models often ignore the emotional element of microblog comments and do not consider that there are different types of relationships between microblog news. In this work, we propose a microblog rumor detection model based on Graph Convolutional Network(GCN). First, the collected microblog news data are used to extract the propagation features, text content features, and user features, and then the comment on emotional features are obtained according to Bidirectional Encoder Representation from Transformers(BERT). In order to fully exploit the relative importance of news, we take news as a node, and construct four graphs with different edge types for four different types of news features. GCN module is used for each graphic structure to learn four kinds of nonlinear characteristics respectively. Finally, the full connection layer automatically learns the weight values of different types of relationships to predict whether the microblog message is false. The experimental results show that the performance of this model is better than that of the baseline model.
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Index Terms
- Detection of Fake News on Microblog Based on Graph Convolutional Network
Index terms have been assigned to the content through auto-classification.
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Published In
December 2022
199 pages
ISBN:9781450399548
DOI:10.1145/3588340
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Publication History
Published: 03 November 2023
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ICBICC 2022
ICBICC 2022: 2022 International Conference on Big data, IoT, and Cloud Computing
December 2 - 4, 2022
Chengdu, China
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