International Journal of Computational Intelligence Systems

Volume 14, Issue 1, 2021, Pages 1053 - 1065

Rumor Detection by Propagation Embedding Based on Graph Convolutional Network

Authors
Dang Thinh VuORCID, Jason J. Jung*, ORCID
Department of Computer Science and Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Korea
*Corresponding author. Email: j2jung@gmail.com
Corresponding Author
Jason J. Jung
Received 28 August 2020, Accepted 1 March 2021, Available Online 16 March 2021.
DOI
10.2991/ijcis.d.210304.002How to use a DOI?
Keywords
Rumor detection; Propagation embedding; Graph convolutional network; Feature aggregation
Abstract

Detecting rumors is an important task in preventing the dissemination of false knowledge within social networks. When a post is propagated in a social network, it typically contains four types of information: i) social interactions, ii) time of publishing, iii) content, and iv) propagation structure. Nonetheless, these information have not been exploited and combined efficiently to distinguish rumors in previous studies. In this research, we propose to detect a rumor post by identifying characteristics based on its propagation patterns and other kinds of information. For the propagation pattern, we suggest using a graph structure to model how a post propagates in social networks, allowing useful knowledge to be derived about a post's pattern of propagation. We then propose a propagation graph embedding method based on a graph convolutional network to learn an embedding vector, representing the propagation pattern and other features of posts in a propagation process. Finally, we classify the learned embedding vectors to different types of rumors by applying a fully connected neural network. Experimental results illustrate that our approach reduces the error of detection by approximately 10% compared with state-of-the-art models. This enhancement proves that the proposed model is efficient on extracting and integrating useful features for discriminating the propagation patterns.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
1053 - 1065
Publication Date
2021/03/16
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.210304.002How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Dang Thinh Vu
AU  - Jason J. Jung
PY  - 2021
DA  - 2021/03/16
TI  - Rumor Detection by Propagation Embedding Based on Graph Convolutional Network
JO  - International Journal of Computational Intelligence Systems
SP  - 1053
EP  - 1065
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210304.002
DO  - 10.2991/ijcis.d.210304.002
ID  - Vu2021
ER  -