Deep Structure Learning for Rumor Detection on Twitter

Huang, Q; Zhou, C; Wu, J; Wang, M; Wang, B

Deep Structure Learning for Rumor Detection on Twitter

Huang, Q Zhou, C Wu, J Wang, M Wang, B

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2019, 2019-July
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Huang, Q
dc.contributor.author	Zhou, C
dc.contributor.author	Wu, J
dc.contributor.author	Wang, M
dc.contributor.author	Wang, B
dc.date	2019-07-14
dc.date.accessioned	2020-06-13T20:37:21Z
dc.date.available	2020-06-13T20:37:21Z
dc.date.issued	2019-07-01
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2019, 2019-July
dc.identifier.isbn	9781728119854
dc.identifier.uri	http://hdl.handle.net/10453/141410
dc.description.abstract	© 2019 IEEE. With the development of social media and the popularity of mobile devices, it becomes increasingly easy to post rumors and spread rumors on social media. Widespread rumors may cause public panic and negative impact on individuals, which makes the automatic detection of rumors become necessary. Most existing methods for automatic rumor detection focus on modeling features related to contents, users and propagation patterns based on feature engineering, but few work consider the existence of graph structural information in the user behavior. In this paper, we propose a model that leverages graph convolutional networks to capture user behavior effectively for rumor detection. Our model is composed of three modules: 1) a user encoder that models users attributes and behaviors based on graph convolutional networks to obtain user representation; 2) a propagation tree encoder, which encodes the structure of the rumor propagation tree as a vector with bridging the content semantics and propagation clues; 3) an integrator that integrates the output of the above modules to identify rumors. Experimental results on two public Twitter datasets show that our model achieves much better performance than the state-of-the-art methods.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks
dc.relation.ispartof	2019 International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/IJCNN.2019.8852468
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Deep Structure Learning for Rumor Detection on Twitter
dc.type	Conference Proceeding
utslib.citation.volume	2019-July
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-06-13T20:37:17Z
pubs.finish-date	2019-07-19
pubs.publication-status	Published
pubs.start-date	2019-07-14
pubs.volume	2019-July

Abstract:

© 2019 IEEE. With the development of social media and the popularity of mobile devices, it becomes increasingly easy to post rumors and spread rumors on social media. Widespread rumors may cause public panic and negative impact on individuals, which makes the automatic detection of rumors become necessary. Most existing methods for automatic rumor detection focus on modeling features related to contents, users and propagation patterns based on feature engineering, but few work consider the existence of graph structural information in the user behavior. In this paper, we propose a model that leverages graph convolutional networks to capture user behavior effectively for rumor detection. Our model is composed of three modules: 1) a user encoder that models users attributes and behaviors based on graph convolutional networks to obtain user representation; 2) a propagation tree encoder, which encodes the structure of the rumor propagation tree as a vector with bridging the content semantics and propagation clues; 3) an integrator that integrates the output of the above modules to identify rumors. Experimental results on two public Twitter datasets show that our model achieves much better performance than the state-of-the-art methods.

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http://hdl.handle.net/10453/141410