Supervised group embedding for rumor detection in social media
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Liu, Yuwei | - |
dc.contributor.author | Chen, Xingming | - |
dc.contributor.author | Rao, Yanghui | - |
dc.contributor.author | Xie, Haoran | - |
dc.contributor.author | Li, Qing | - |
dc.contributor.author | Zhang, Jun | - |
dc.contributor.author | Zhao, Yingchao | - |
dc.contributor.author | Wang, Fu Lee | - |
dc.date.accessioned | 2023-12-08T09:32:09Z | - |
dc.date.available | 2023-12-08T09:32:09Z | - |
dc.date.issued | 2019-06 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115845 | - |
dc.description.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. © Springer Nature Switzerland AG 2019. | - |
dc.format.extent | 15 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Springer Verlag | - |
dc.title | Supervised group embedding for rumor detection in social media | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1007/978-3-030-19274-7_11 | - |
dc.identifier.scopusid | 2-s2.0-85065497389 | - |
dc.identifier.wosid | 000719417400011 | - |
dc.identifier.bibliographicCitation | Web Engineering 19th International Conference, ICWE 2019, Daejeon, South Korea, June 11–14, 2019, Proceedings, v.11496, pp 139 - 153 | - |
dc.citation.title | Web Engineering 19th International Conference, ICWE 2019, Daejeon, South Korea, June 11–14, 2019, Proceedings | - |
dc.citation.volume | 11496 | - |
dc.citation.startPage | 139 | - |
dc.citation.endPage | 153 | - |
dc.type.docType | Conference paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.subject.keywordAuthor | Convolutional Neural Network | - |
dc.subject.keywordAuthor | Rumor detection | - |
dc.subject.keywordAuthor | Social media | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-030-19274-7_11 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.