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dc.contributor.authorDong, Yuchengen
dc.contributor.authorDing, Zhaogangen
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2017-03-06T15:03:13Z
dc.date.available2017-03-06T15:03:13Z
dc.date.issued2017
dc.identifier.citationDong, Y., Ding, Z., Chiclana, F. and Herrera-Viedma, E. (2017) Dynamics of public opinions in an online and offline social network. IEEE Transactions on Big Data.en
dc.identifier.urihttp://hdl.handle.net/2086/13421
dc.description.abstractWith the development of the information and Internet technology, public opinions with big data will rapidly emerge in an online-offline social network, and an inefficient management of public opinions often will lead to security crises for either firms or governments. To unveil the interaction mechanism among a large number of agents between the online and offline social networks, this paper proposes a public opinion dynamics model in an online-offline social network context. Within a theoretical framework, the analytical conditions to form a consensus in the public opinion dynamics model is investigated. Furthermore, extensive simulations to investigate how the online agents impact the dynamics of public opinion formation are conducted, which unfold that online agents shorten the steady-state time, decrease the number of opinion clusters, and smooth opinion changes in the opinion dynamics. The increase of online agents often enhances these effects. The results in this paper can provide a basis for the management of public opinions in the Internet age.en
dc.language.isoenen
dc.subjectOpinion dynamicsen
dc.subjectSocial networken
dc.subjectConsensusen
dc.subjectSecurityen
dc.subjectOnline and offline contexten
dc.subjectBig dataen
dc.titleDynamics of public opinions in an online and offline social networken
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1109/tbdata.2017.2676810
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThis work was supported by the grants (Nos. 71171160 and 71571124) from NSF of China, the grant (No. skqy201606) from Sichuan University, the grants (Nos. TIN2013-40658-P and TIN2016-75850-R) from the FEDER funds, and the grant (No. TIC-5991) from the Andalusian Excellence Project.en
dc.projectidThis work was supported by the grants (Nos. 71171160 and 71571124) from NSF of China, the grant (No. skqy201606) from Sichuan University, the grants (Nos. TIN2013-40658-P and TIN2016-75850-R) from the FEDER funds, and the grant (No. TIC-5991) from the Andalusian Excellence Project.en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-02-25en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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