Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.

Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely. Thus, detecting the rumors and preventing their spread became an essential task. Some of the recent d...

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Autores principales:	Jiho Choi, Taewook Ko, Younhyuk Choi, Hyungho Byun, Chong-Kwon Kim
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9dbc1c320ab74548974320229e3cf3db
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id	oai:doaj.org-article:9dbc1c320ab74548974320229e3cf3db
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spelling	oai:doaj.org-article:9dbc1c320ab74548974320229e3cf3db2021-12-02T20:17:51ZDynamic graph convolutional networks with attention mechanism for rumor detection on social media.1932-620310.1371/journal.pone.0256039https://doaj.org/article/9dbc1c320ab74548974320229e3cf3db2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256039https://doaj.org/toc/1932-6203Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely. Thus, detecting the rumors and preventing their spread became an essential task. Some of the recent deep learning-based rumor detection methods, such as Bi-Directional Graph Convolutional Networks (Bi-GCN), represent rumor using the completed stage of the rumor diffusion and try to learn the structural information from it. However, these methods are limited to represent rumor propagation as a static graph, which isn't optimal for capturing the dynamic information of the rumors. In this study, we propose novel graph convolutional networks with attention mechanisms, named Dynamic GCN, for rumor detection. We first represent rumor posts with their responsive posts as dynamic graphs. The temporal information is used to generate a sequence of graph snapshots. The representation learning on graph snapshots with attention mechanism captures both structural and temporal information of rumor spreads. The conducted experiments on three real-world datasets demonstrate the superiority of Dynamic GCN over the state-of-the-art methods in the rumor detection task.Jiho ChoiTaewook KoYounhyuk ChoiHyungho ByunChong-Kwon KimPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256039 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jiho Choi Taewook Ko Younhyuk Choi Hyungho Byun Chong-Kwon Kim Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.
description	Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely. Thus, detecting the rumors and preventing their spread became an essential task. Some of the recent deep learning-based rumor detection methods, such as Bi-Directional Graph Convolutional Networks (Bi-GCN), represent rumor using the completed stage of the rumor diffusion and try to learn the structural information from it. However, these methods are limited to represent rumor propagation as a static graph, which isn't optimal for capturing the dynamic information of the rumors. In this study, we propose novel graph convolutional networks with attention mechanisms, named Dynamic GCN, for rumor detection. We first represent rumor posts with their responsive posts as dynamic graphs. The temporal information is used to generate a sequence of graph snapshots. The representation learning on graph snapshots with attention mechanism captures both structural and temporal information of rumor spreads. The conducted experiments on three real-world datasets demonstrate the superiority of Dynamic GCN over the state-of-the-art methods in the rumor detection task.
format	article
author	Jiho Choi Taewook Ko Younhyuk Choi Hyungho Byun Chong-Kwon Kim
author_facet	Jiho Choi Taewook Ko Younhyuk Choi Hyungho Byun Chong-Kwon Kim
author_sort	Jiho Choi
title	Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.
title_short	Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.
title_full	Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.
title_fullStr	Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.
title_full_unstemmed	Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.
title_sort	dynamic graph convolutional networks with attention mechanism for rumor detection on social media.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9dbc1c320ab74548974320229e3cf3db
work_keys_str_mv	AT jihochoi dynamicgraphconvolutionalnetworkswithattentionmechanismforrumordetectiononsocialmedia AT taewookko dynamicgraphconvolutionalnetworkswithattentionmechanismforrumordetectiononsocialmedia AT younhyukchoi dynamicgraphconvolutionalnetworkswithattentionmechanismforrumordetectiononsocialmedia AT hyunghobyun dynamicgraphconvolutionalnetworkswithattentionmechanismforrumordetectiononsocialmedia AT chongkwonkim dynamicgraphconvolutionalnetworkswithattentionmechanismforrumordetectiononsocialmedia
_version_	1718374361626312704

Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.

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