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Dataset Twitter - 2016 UCI Track Cycling World Championships

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Dataset Twitter - 2016 UCI Track Cycling World Championships

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dc.contributor.author Lamirán Palomares, José María es_ES
dc.contributor.author Baviera Puig, Tomás es_ES
dc.contributor.author Baviera Puig, Maria Amparo es_ES
dc.date.accessioned 2021-02-25T11:29:07Z
dc.date.available 2019-03-20T12:31:30Z
dc.date.available 2021-02-25T11:29:07Z
dc.date.issued 2019-03-20
dc.identifier.uri http://hdl.handle.net/10251/118266
dc.description We analysed 55,572 tweets (English and non-English) sent initially by 20,303 users, which later fell to 20,175 users after we detected 128 errors or duplicates, among other issues. Once the database was refined, we identified the interactions among the users in the conversation through mentions and re-tweets. Although we cannot assure that we have all the tweets specified with that condition (with the hashtag #TWC2016), at least we have a big sample that we consider enough for our research. The hashtag (#TWC2016) was the same as the one used by The Women’s Conference 2016. This Conference was held in December 2016. Though the dates did not coincide, we checked that there was no tweet of this event in our database. es_ES
dc.description.abstract [EN] Social media platforms have had a significant impact on the public image of sports in recent years. Through the relational dynamics of the communication on these networks, many users have emerged whose opinions can exert a great deal of influence on public conversation online. This research aims to identify the influential Twitter users during the 2016 UCI Track Cycling World Championships using different variables which, in turn, represent different dimensions of influence (popularity, activity and authority). Mathematical variables of the social network analysis and variables provided by Twitter and Google are compared. As a result, no single variable assessed is sufficient to identify the different kinds of influential Twitter users. Having a certain type of account is not enough to make a user an influencer if they do not engage (actively or passively) in the conversation. Choosing some influencers or others will depend on the objectives pursued. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València
dc.relation.uri https://doi.org/10.3390/socsci8050141
dc.relation.uri https://doi.org/10.3390/socsci9100169
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Twitter es_ES
dc.subject Influence es_ES
dc.subject Social Network Analysis es_ES
dc.subject Social media es_ES
dc.subject Influencers es_ES
dc.subject Sports es_ES
dc.subject Deportes es_ES
dc.subject Redes sociales es_ES
dc.subject Influencia es_ES
dc.subject Análisis de redes sociales es_ES
dc.subject.classification COMERCIALIZACION E INVESTIGACION DE MERCADOS es_ES
dc.subject.classification ECONOMIA, SOCIOLOGIA Y POLITICA AGRARIA es_ES
dc.title Dataset Twitter - 2016 UCI Track Cycling World Championships es_ES
dc.type Dataset es_ES
dc.identifier.doi 10.4995/Dataset/10251/118266
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Economía y Ciencias Sociales - Departament d'Economia i Ciències Socials es_ES
dc.description.bibliographicCitation Lamirán Palomares, JM.; Baviera Puig, T.; Baviera Puig, MA. (2019). Dataset Twitter - 2016 UCI Track Cycling World Championships. https://doi.org/10.4995/Dataset/10251/118266 es_ES
dc.type.version info:eu-repo/semantics/submittedVersion es_ES


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