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dc.contributor.authorFernández de Landa, Joseba
dc.contributor.authorAgerri Gascón, Rodrigo
dc.contributor.authorAlegría Loinaz, Iñaki
dc.date.accessioned2020-02-04T08:22:48Z
dc.date.available2020-02-04T08:22:48Z
dc.date.issued2019-06-13
dc.identifier.citationInformation 10(6) : (2019) // Article ID 212es_ES
dc.identifier.issn2078-2489
dc.identifier.urihttp://hdl.handle.net/10810/40404
dc.description.abstractSocial networks like Twitter are increasingly important in the creation of new ways of communication. They have also become useful tools for social and linguistic research due to the massive amounts of public textual data available. This is particularly important for less resourced languages, as it allows to apply current natural language processing techniques to large amounts of unstructured data. In this work, we study the linguistic and social aspects of young and adult people's behaviour based on their tweets' contents and the social relations that arise from them. With this objective in mind, we have gathered over 10 million tweets from more than 8000 users. First, we classified each user in terms of its life stage (young/adult) according to the writing style of their tweets. Second, we applied topic modelling techniques to the personal tweets to find the most popular topics according to life stages. Third, we established the relations and communities that emerge based on the retweets. We conclude that using large amounts of unstructured data provided by Twitter facilitates social research using computational techniques such as natural language processing, giving the opportunity both to segment communities based on demographic characteristics and to discover how they interact or relate to them.es_ES
dc.description.sponsorshipThe second author is funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER, UE), under the project CROSSTEXT (TIN2015-72646-EXP) and the Ramon y Cajal Fellowship RYC-2017-23647. He also acknowledges the support of the BBVA Big Data 2018 "BigKnowledge for TextMining (BigKnowledge)" project.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/TIN2015-72646-EXPes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/RYC-2017-23647es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectsocial informaticses_ES
dc.subjectsocial networkses_ES
dc.subjecttopic modellinges_ES
dc.subjectrelationses_ES
dc.subjectless resourced languageses_ES
dc.subjecttext classificationes_ES
dc.subjectinformation extractiones_ES
dc.subjectnatural language processinges_ES
dc.subjectbenchmarkes_ES
dc.titleLarge Scale Linguistic Processing of Tweets to Understand Social Interactions among Speakers of Less Resourced Languages: The Basque Casees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holderThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0)es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://www.mdpi.com/2078-2489/10/6/212es_ES
dc.identifier.doi10.3390/info10060212
dc.departamentoesArquitectura y Tecnología de Computadoreses_ES
dc.departamentoeuKonputagailuen Arkitektura eta Teknologiaes_ES


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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0)
Except where otherwise noted, this item's license is described as This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0)