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dc.contributor.authorPALOMINO, MARCO
dc.contributor.authorRibac, Q
dc.contributor.authormasala, giovanni
dc.contributor.editorFujita H
dc.contributor.editorHerrera-Viedma E
dc.date.accessioned2018-09-25T08:31:05Z
dc.date.available2018-09-25T08:31:05Z
dc.date.issued2018
dc.identifier.isbn978-1-61499-899-0
dc.identifier.issn0922-6389
dc.identifier.issn1879-8314
dc.identifier.urihttp://hdl.handle.net/10026.1/12416
dc.description.abstract

We are inundated with data—companies such as Twitter deal with petabytes of information on a daily basis. However, some users, especially the new ones, often find it difficult to cope with the overwhelming and disorganised deluge of information. Scientists have already worked out ways to identify Twitter trending topics, as a means to index information and make sense of it. However, we know little about the impact on trending topics of various intrinsic factors associated with the Twitter ecosystem. For example, anecdotal evidence suggests that trending topics are characterised by highly polarised tweets, or that large audiences typically host the emergence of trending topics. However, no study has yet addressed these issues formally. To remedy this situation, we have launched an investigation on the nature of trending topics. Our initial observations indicate that there is a correlation between strong sentiment polarity and the emergence of trending topics—we can also confirm that the strength of the polarity drops as the trending topics fade away. Conversely, our experiments highlight that there is no correlation between the size of a Twitter audience and the rise of trending topics.

dc.format.extent1004-1017
dc.language.isoen
dc.publisherIOS Press
dc.subjectclustering
dc.subjectnews information retrieval
dc.subjectsentiment analysis
dc.subjectTF-IDF
dc.subjecttrending topics
dc.subjectTwitter
dc.titleThe Nature of Twitter Trending Topics Analysing Intrinsic Factors Associated with the Twitter Ecosystem
dc.typeconference
dc.typeConference Proceeding
plymouth.author-urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000467457200078&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume303
plymouth.publisher-urlhttp://www.informatik.uni-trier.de/~ley/db/conf/somet/somet2018.html
plymouth.publication-statusPublished
plymouth.journalNEW TRENDS IN INTELLIGENT SOFTWARE METHODOLOGIES, TOOLS AND TECHNIQUES (SOMET_18)
dc.identifier.doi10.3233/978-1-61499-900-3-1004
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2018-01-01
dc.rights.embargodate2022-1-25
dc.identifier.eissn1879-8314
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.3233/978-1-61499-900-3-1004
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018
rioxxterms.typeConference Paper/Proceeding/Abstract


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