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A topic detection and visualisation system on social media posts

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dc.contributor.author Andreadis, Stelios
dc.contributor.author Gialampoukidis, Ilias
dc.contributor.author Vrochidis, Stefanos
dc.contributor.author Kompatsiaris, Ioannis
dc.date.accessioned 2018-03-21T16:33:16Z
dc.date.available 2018-03-21T16:33:16Z
dc.date.issued 2017
dc.identifier.citation Andreadis S, Gialampoukidis I, Vrochidis S, Kompatsiaris I. A topic detection and visualisation system on social media posts. In: Kompatsiaris I, Cave J, Satsiou A, Carle G, Passani A, Kontopoulos E, Diplaris S, McMillan D, editors. Internet Science. 4th International Conference, INSCI 2017 Proceedings; 2017 Nov 22-24; Thessaloniki, Greece. Cham: Springer; 2017. p. 421-7. (LNCS; no. 10673). DOI: 10.1007/978-3-319-70284-1_33
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10230/34229
dc.description Comunicació presentada a: Internet Science. 4th International Conference, INSCI 2017 celebrada del 22 al 24 de novembre de 2017 a Thessaloniki, Grècia.
dc.description.abstract Large amounts of social media posts are produced on a daily basis and monitoring all of them is a challenging task. In this direction we demonstrate a topic detection and visualisation tool in Twitter data, which filters Twitter posts by topic or keyword, in two different languages; German and Turkish. The system is based on state-of-the-art news clustering methods and the tool has been created to handle streams of recent news information in a fast and user-friendly way. The user interface and user-system interaction examples are presented in detail.
dc.description.sponsorship This work was supported by the EC-funded projects H2020-645012 (KRISTINA) and H2020-700475 (beAWARE).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Kompatsiaris I, Cave J, Satsiou A, Carle G, Passani A, Kontopoulos E, Diplaris S, McMillan D, editors. Internet Science. 4th International Conference, INSCI 2017 Proceedings; 2017 Nov 22-24; Thessaloniki, Greece. Cham: Springer; 2017. p. 421-7. (LNCS; no. 10673).
dc.rights © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-70284-1_33
dc.title A topic detection and visualisation system on social media posts
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1007/978-3-319-70284-1_33
dc.subject.keyword Topic detection and visualisation
dc.subject.keyword Twitter posts
dc.subject.keyword Keyword-based search
dc.subject.keyword Topic-based filtering
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020//700475
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion


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