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 ProceedingsConference paper (refereed)Research

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 languageEnglish
Title of host publicationWeb Engineering - 19th International Conference, ICWE 2019, Proceedings
EditorsMaxim BAKAEV, Flavius FRASINCAR, In-Young KO
Place of PublicationSwitzerland
PublisherSpringer Nature Switzerland AG
Pages139-153
Number of pages15
ISBN (Electronic)9783030192747
ISBN (Print)9783030192730
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event19th International Conference on Web Engineering - Daejeon, Korea, Republic of
Duration: 11 Jun 201914 Jun 2019
https://icwe2019.webengineering.org/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11496 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Web Engineering
Abbreviated titleICWE 2019
CountryKorea, Republic of
Period11/06/1914/06/19
Internet address

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Keywords

  • Convolutional Neural Network
  • Rumor detection
  • Social media

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