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dc.contributor.authorNguyen, Thanh Tam
dc.contributor.authorWeidlich, Matthias
dc.contributor.authorZheng, Bolong
dc.contributor.authorYin, Hongzhi
dc.contributor.authorNguyen, Quoc Viet Hung
dc.contributor.authorStantic, Bela
dc.date.accessioned2020-05-28T21:46:02Z
dc.date.available2020-05-28T21:46:02Z
dc.date.issued2019
dc.identifier.issn2150-8097
dc.identifier.doi10.14778/3329772.3329778
dc.identifier.urihttp://hdl.handle.net/10072/394201
dc.description.abstractSocial platforms became a major source of rumours. While rumours can have severe real-world implications, their detection is notoriously hard: Content on social platforms is short and lacks semantics; it spreads quickly through a dynamically evolving network; and without considering the context of content, it may be impossible to arrive at a truthful interpretation. Traditional approaches to rumour detection, however, exploit solely a single content modality, e.g., social media posts, which limits their detection accuracy. In this paper, we cope with the aforementioned challenges by means of a multi-modal approach to rumour detection that identifies anomalies in both, the entities (e.g., users, posts, and hashtags) of a social platform and their relations. Based on local anomalies, we show how to detect rumours at the network level, following a graph-based scan approach. In addition, we propose incremental methods, which enable us to detect rumours using streaming data of social platforms. We illustrate the effectiveness and efficiency of our approach with a real-world dataset of 4M tweets with more than 1000 rumours.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofpagefrom1016
dc.relation.ispartofpageto1029
dc.relation.ispartofissue9
dc.relation.ispartofjournalProceedings of the VLDB Endowment
dc.relation.ispartofvolume12
dc.subject.fieldofresearchComputation Theory and Mathematics
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchLibrary and Information Studies
dc.subject.fieldofresearchcode0802
dc.subject.fieldofresearchcode0806
dc.subject.fieldofresearchcode0807
dc.subject.keywordsScience & Technology
dc.subject.keywordsTechnology
dc.subject.keywordsComputer Science, Information Systems
dc.subject.keywordsComputer Science, Theory & Methods
dc.subject.keywordsComputer Science
dc.titleFrom Anomaly Detection to Rumour Detection using Data Streams of Social Platforms
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationNguyen, TT; Weidlich, M; Zheng, B; Yin, H; Nguyen, QVH; Stantic, B, From Anomaly Detection to Rumour Detection using Data Streams of Social Platforms, Proceedings of the VLDB Endowment, 2019, 12 (9), pp. 1016-1029
dc.date.updated2020-05-28T02:27:22Z
dc.description.versionPost-print
gro.rights.copyright© VLDB Endowment, 2019. Published by ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Vol. 12, No. 9, pp. 1016-1029, 2019, 10.14778/3329772.3329778
gro.hasfulltextFull Text
gro.griffith.authorStantic, Bela
gro.griffith.authorNguyen, Henry


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