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dc.contributor.authorShi, Lei-Lei
dc.contributor.authorLiu, Lu
dc.contributor.authorWu, Yan
dc.contributor.authorJiang, Liang
dc.contributor.authorKazim, Muhammad
dc.contributor.authorAli, Haider
dc.contributor.authorPanneerselvam, John
dc.date.accessioned2020-02-13T15:39:36Z
dc.date.available2020-02-13T15:39:36Z
dc.date.issued2019-05-21
dc.identifier.citationShi, L.L., Liu, L., Wu, Y., Jiang, L., Kazim, M., Ali, H. and Panneerselvam, J. (2019) Human-centric cyber social computing model for hot-event detection and propagation. IEEE Transactions on Computational Social Systems, 6(5), pp.1042-1050.en
dc.identifier.issn2329-924X
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/19179
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractMicroblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectEvent detectionen
dc.subjectevent propagationen
dc.subjecthuman centricen
dc.subjectsocial computing.en
dc.titleHuman-Centric Cyber Social Computing Model for Hot-Event Detection and Propagationen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1109/tcss.2019.2913783
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2019
dc.researchinstituteCyber Technology Institute (CTI)en
dc.exception.ref2021codes254aen
dc.funder.otherThis work was partially supported by the Natural Science Foundation of Jiangsu Province under Grant BK20170069, and UK-Jiangsu 20-20 World Class University Initiative program.en


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