Preprint
Article

Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

This version is not peer-reviewed.

Submitted:

18 November 2020

Posted:

19 November 2020

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Abstract
Identifying fake news on the media has been an important issue. This is especially true considering the wide spread of rumors on the popular social networks such as Twitter. Various kinds of techniques have been proposed to detect rumors. In this work, we study the application of graph neural networks for the task of rumor detection, and present a simplified new architecture to classify rumors. Numerical experiments show that the proposed simple network has comparable to or even better performance than state-of-the art graph convolutional networks, while having significantly reduced the computational complexity.
Keywords: 
Rumor detection; Graph neural network; Artificial intelligence
Subject: 
Engineering  -   Automotive Engineering
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

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