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Rumor detection based on propagation graph neural network with attention mechanism

Rumors on social media have always been an important issue that seriously endangers social security. Researches on timely and effective detection of rumors have aroused lots of interest in both academia and industry. At present, most existing methods identify rumors based solely on the linguistic in...

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Autores principales: Wu, Zhiyuan, Pi, Dechang, Chen, Junfu, Xie, Meng, Cao, Jianjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274137/
https://www.ncbi.nlm.nih.gov/pubmed/32565619
http://dx.doi.org/10.1016/j.eswa.2020.113595
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author Wu, Zhiyuan
Pi, Dechang
Chen, Junfu
Xie, Meng
Cao, Jianjun
author_facet Wu, Zhiyuan
Pi, Dechang
Chen, Junfu
Xie, Meng
Cao, Jianjun
author_sort Wu, Zhiyuan
collection PubMed
description Rumors on social media have always been an important issue that seriously endangers social security. Researches on timely and effective detection of rumors have aroused lots of interest in both academia and industry. At present, most existing methods identify rumors based solely on the linguistic information without considering the temporal dynamics and propagation patterns. In this work, we aim to solve rumor detection task under the framework of representation learning. We first propose a novel way to construct the propagation graph by following the propagation structure (who replies to whom) of posts on Twitter. Then we propose a gated graph neural network based algorithm called PGNN, which can generate powerful representations for each node in the propagation graph. The proposed PGNN algorithm repeatedly updates node representations by exchanging information between the neighbor nodes via relation paths within a limited time steps. On this basis, we propose two models, namely GLO-PGNN (rumor detection model based on the global embedding with propagation graph neural network) and ENS-PGNN (rumor detection model based on the ensemble learning with propagation graph neural network). They respectively adopt different classification strategies for rumor detection task, and further improve the performance by including attention mechanism to dynamically adjust the weight of each node in the propagation graph. Experiments on a real-world Twitter dataset demonstrate that our proposed models achieve much better performance than state-of-the-art methods both on the rumor detection task and early detection task.
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spelling pubmed-72741372020-06-05 Rumor detection based on propagation graph neural network with attention mechanism Wu, Zhiyuan Pi, Dechang Chen, Junfu Xie, Meng Cao, Jianjun Expert Syst Appl Article Rumors on social media have always been an important issue that seriously endangers social security. Researches on timely and effective detection of rumors have aroused lots of interest in both academia and industry. At present, most existing methods identify rumors based solely on the linguistic information without considering the temporal dynamics and propagation patterns. In this work, we aim to solve rumor detection task under the framework of representation learning. We first propose a novel way to construct the propagation graph by following the propagation structure (who replies to whom) of posts on Twitter. Then we propose a gated graph neural network based algorithm called PGNN, which can generate powerful representations for each node in the propagation graph. The proposed PGNN algorithm repeatedly updates node representations by exchanging information between the neighbor nodes via relation paths within a limited time steps. On this basis, we propose two models, namely GLO-PGNN (rumor detection model based on the global embedding with propagation graph neural network) and ENS-PGNN (rumor detection model based on the ensemble learning with propagation graph neural network). They respectively adopt different classification strategies for rumor detection task, and further improve the performance by including attention mechanism to dynamically adjust the weight of each node in the propagation graph. Experiments on a real-world Twitter dataset demonstrate that our proposed models achieve much better performance than state-of-the-art methods both on the rumor detection task and early detection task. Elsevier Ltd. 2020-11-15 2020-06-05 /pmc/articles/PMC7274137/ /pubmed/32565619 http://dx.doi.org/10.1016/j.eswa.2020.113595 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Wu, Zhiyuan
Pi, Dechang
Chen, Junfu
Xie, Meng
Cao, Jianjun
Rumor detection based on propagation graph neural network with attention mechanism
title Rumor detection based on propagation graph neural network with attention mechanism
title_full Rumor detection based on propagation graph neural network with attention mechanism
title_fullStr Rumor detection based on propagation graph neural network with attention mechanism
title_full_unstemmed Rumor detection based on propagation graph neural network with attention mechanism
title_short Rumor detection based on propagation graph neural network with attention mechanism
title_sort rumor detection based on propagation graph neural network with attention mechanism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274137/
https://www.ncbi.nlm.nih.gov/pubmed/32565619
http://dx.doi.org/10.1016/j.eswa.2020.113595
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AT xiemeng rumordetectionbasedonpropagationgraphneuralnetworkwithattentionmechanism
AT caojianjun rumordetectionbasedonpropagationgraphneuralnetworkwithattentionmechanism