Supervised group embedding for rumor detection in social media

Yuwei LIU; Xingming CHEN; Yanghui RAO; Haoran XIE; Qing LI; Jun ZHANG; Yingchao ZHAO; Fu Lee WANG

doi:10.1007/978-3-030-19274-7_11

Supervised group embedding for rumor detection in social media

Yuwei LIU, Xingming CHEN, Yanghui RAO^*, Haoran XIE, Qing LI, Jun ZHANG, Yingchao ZHAO, Fu Lee WANG

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research

4 Citations (Scopus)

Abstract

To detect rumors automatically in social media, methods based on recurrent neural network and convolutional neural network have been proposed. These methods split a stream of posts related to an event into several groups along time, and represent each group using unsupervised methods such as paragraph vector. However, many posts in a group (e.g., retweeted posts) do not contribute much to rumor detection, which deteriorates the performance of rumor detection based on unsupervised group embedding. In this paper, we propose a Supervised Group Embedding based Rumor Detection (SGERD) model that considers both textual and temporal information. Particularly, SGERD exploits post-level textual information to generate group embeddings, and is able to identify salient posts for further analysis. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed model.

Original language	English
Title of host publication	Web Engineering - 19th International Conference, ICWE 2019, Proceedings
Editors	Maxim BAKAEV, Flavius FRASINCAR, In-Young KO
Place of Publication	Switzerland
Publisher	Springer Nature Switzerland AG
Pages	139-153
Number of pages	15
ISBN (Electronic)	9783030192747
ISBN (Print)	9783030192730
DOIs	https://doi.org/10.1007/978-3-030-19274-7_11
Publication status	Published - 2019
Externally published	Yes
Event	19th International Conference on Web Engineering - Daejeon, Korea, Republic of Duration: 11 Jun 2019 → 14 Jun 2019 https://icwe2019.webengineering.org/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11496 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Web Engineering
Abbreviated title	ICWE 2019
Country/Territory	Korea, Republic of
Period	11/06/19 → 14/06/19
Internet address	https://icwe2019.webengineering.org/

Funding

This research has been supported in part by the National Natural Science Foundation of China (U1611264), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), the Individual Research Scheme of the Dean’s Research Fund 2017–2018 (FLASS/DRF/IRS-8), Top-up Fund for General Research Fund/Early Career Scheme (TFG-3) and Seed Fund for General Research Fund/Early Career Scheme (SFG-6) of the 2018 Dean’s Research Fund to MIT Department, Small Grant for Academic Staff (MIT/SGA05/18-19) of The Education University of Hong Kong, and a Collaborative Research Grant (project no. C1031-18G) from the Research Grants Council of Hong Kong SAR.

Keywords

Convolutional Neural Network
Rumor detection
Social media

Access to Document

10.1007/978-3-030-19274-7_11

Cite this

LIU, Y., CHEN, X., RAO, Y., XIE, H., LI, Q., ZHANG, J., ZHAO, Y., & WANG, F. L. (2019). Supervised group embedding for rumor detection in social media. In M. BAKAEV, F. FRASINCAR, & I.-Y. KO (Eds.), Web Engineering - 19th International Conference, ICWE 2019, Proceedings (pp. 139-153). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11496 LNCS). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-19274-7_11

LIU, Yuwei ; CHEN, Xingming ; RAO, Yanghui et al. / Supervised group embedding for rumor detection in social media. Web Engineering - 19th International Conference, ICWE 2019, Proceedings. editor / Maxim BAKAEV ; Flavius FRASINCAR ; In-Young KO. Switzerland : Springer Nature Switzerland AG, 2019. pp. 139-153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{44d435642a9b49ee87ccd08ea3799a49,

title = "Supervised group embedding for rumor detection in social media",

abstract = "To detect rumors automatically in social media, methods based on recurrent neural network and convolutional neural network have been proposed. These methods split a stream of posts related to an event into several groups along time, and represent each group using unsupervised methods such as paragraph vector. However, many posts in a group (e.g., retweeted posts) do not contribute much to rumor detection, which deteriorates the performance of rumor detection based on unsupervised group embedding. In this paper, we propose a Supervised Group Embedding based Rumor Detection (SGERD) model that considers both textual and temporal information. Particularly, SGERD exploits post-level textual information to generate group embeddings, and is able to identify salient posts for further analysis. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed model.",

keywords = "Convolutional Neural Network, Rumor detection, Social media",

author = "Yuwei LIU and Xingming CHEN and Yanghui RAO and Haoran XIE and Qing LI and Jun ZHANG and Yingchao ZHAO and WANG, {Fu Lee}",

year = "2019",

doi = "10.1007/978-3-030-19274-7_11",

language = "English",

isbn = "9783030192730",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Maxim BAKAEV and Flavius FRASINCAR and In-Young KO",

booktitle = "Web Engineering - 19th International Conference, ICWE 2019, Proceedings",

address = "Switzerland",

note = "19th International Conference on Web Engineering, ICWE 2019 ; Conference date: 11-06-2019 Through 14-06-2019",

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LIU, Y, CHEN, X, RAO, Y, XIE, H, LI, Q, ZHANG, J, ZHAO, Y & WANG, FL 2019, Supervised group embedding for rumor detection in social media. in M BAKAEV, F FRASINCAR & I-Y KO (eds), Web Engineering - 19th International Conference, ICWE 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11496 LNCS, Springer Nature Switzerland AG, Switzerland, pp. 139-153, 19th International Conference on Web Engineering, Korea, Republic of, 11/06/19. https://doi.org/10.1007/978-3-030-19274-7_11

Supervised group embedding for rumor detection in social media. / LIU, Yuwei; CHEN, Xingming; RAO, Yanghui et al.
Web Engineering - 19th International Conference, ICWE 2019, Proceedings. ed. / Maxim BAKAEV; Flavius FRASINCAR; In-Young KO. Switzerland: Springer Nature Switzerland AG, 2019. p. 139-153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11496 LNCS).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research

TY - GEN

T1 - Supervised group embedding for rumor detection in social media

AU - LIU, Yuwei

AU - CHEN, Xingming

AU - RAO, Yanghui

AU - XIE, Haoran

AU - LI, Qing

AU - ZHANG, Jun

AU - ZHAO, Yingchao

AU - WANG, Fu Lee

PY - 2019

Y1 - 2019

N2 - To detect rumors automatically in social media, methods based on recurrent neural network and convolutional neural network have been proposed. These methods split a stream of posts related to an event into several groups along time, and represent each group using unsupervised methods such as paragraph vector. However, many posts in a group (e.g., retweeted posts) do not contribute much to rumor detection, which deteriorates the performance of rumor detection based on unsupervised group embedding. In this paper, we propose a Supervised Group Embedding based Rumor Detection (SGERD) model that considers both textual and temporal information. Particularly, SGERD exploits post-level textual information to generate group embeddings, and is able to identify salient posts for further analysis. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed model.

AB - To detect rumors automatically in social media, methods based on recurrent neural network and convolutional neural network have been proposed. These methods split a stream of posts related to an event into several groups along time, and represent each group using unsupervised methods such as paragraph vector. However, many posts in a group (e.g., retweeted posts) do not contribute much to rumor detection, which deteriorates the performance of rumor detection based on unsupervised group embedding. In this paper, we propose a Supervised Group Embedding based Rumor Detection (SGERD) model that considers both textual and temporal information. Particularly, SGERD exploits post-level textual information to generate group embeddings, and is able to identify salient posts for further analysis. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed model.

KW - Convolutional Neural Network

KW - Rumor detection

KW - Social media

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DO - 10.1007/978-3-030-19274-7_11

M3 - Conference paper (refereed)

SN - 9783030192730

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 139

EP - 153

BT - Web Engineering - 19th International Conference, ICWE 2019, Proceedings

A2 - BAKAEV, Maxim

A2 - FRASINCAR, Flavius

A2 - KO, In-Young

PB - Springer Nature Switzerland AG

CY - Switzerland

T2 - 19th International Conference on Web Engineering

Y2 - 11 June 2019 through 14 June 2019

ER -

LIU Y, CHEN X, RAO Y, XIE H, LI Q, ZHANG J et al. Supervised group embedding for rumor detection in social media. In BAKAEV M, FRASINCAR F, KO IY, editors, Web Engineering - 19th International Conference, ICWE 2019, Proceedings. Switzerland: Springer Nature Switzerland AG. 2019. p. 139-153. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2019 Apr 26. doi: 10.1007/978-3-030-19274-7_11

Supervised group embedding for rumor detection in social media

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