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Does the type of event influence how user interactions evolve on Twitter?

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Does the type of event influence how user interactions evolve on Twitter?

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dc.contributor.author Del Val Noguera, Elena es_ES
dc.contributor.author Rebollo Pedruelo, Miguel es_ES
dc.contributor.author Botti Navarro, Vicente Juan es_ES
dc.date.accessioned 2016-04-25T13:54:41Z
dc.date.available 2016-04-25T13:54:41Z
dc.date.issued 2015-05-11
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10251/62891
dc.description This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited es_ES
dc.description.abstract The number of people using on-line social networks as a new way of communication is continually increasing. The messages that a user writes in these networks and his/her interactions with other users leave a digital trace that is recorded. Thanks to this fact and the use of network theory, the analysis of messages, user interactions, and the complex structures that emerge is greatly facilitated. In addition, information generated in on-line social networks is labeled temporarily, which makes it possible to go a step further analyzing the dynamics of the interaction patterns. In this article, we present an analysis of the evolution of user interactions that take place in television, socio-political, conference, and keynote events on Twitter. Interactions have been modeled as networks that are annotated with the time markers. We study changes in the structural properties at both the network level and the node level. As a result of this analysis, we have detected patterns of network evolution and common structural features as well as differences among the events. es_ES
dc.description.sponsorship The author(s) received specific funding for this work from the research group (Grupo de Inteligencia Informatica e Inteligencia Artificial) where the authors are currently working. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. en_EN
dc.language Inglés es_ES
dc.publisher Public Library of Science es_ES
dc.relation.ispartof PLoS ONE es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Social networks analysis es_ES
dc.subject Artificial Intelligence es_ES
dc.subject Complex systems es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.subject.classification BIBLIOTECONOMIA Y DOCUMENTACION es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Does the type of event influence how user interactions evolve on Twitter? es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1371/journal.pone.0124049
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2012-36586-C03-01/ES/SOCIEDADES HUMANO-AGENTE: DISEÑO, FORMACION Y COORDINACION/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEOII%2F2013%2F019/ES/ HUMAN-LIKE COMPUTATIONAL MODELS FOR AGENT-BASED COMPUTATIONAL ECONOMICS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//SP20140800/ES/Redes Sociales Virtuales/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Del Val Noguera, E.; Rebollo Pedruelo, M.; Botti Navarro, VJ. (2015). Does the type of event influence how user interactions evolve on Twitter?. PLoS ONE. 10(5):21-53. https://doi.org/10.1371/journal.pone.0124049 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1371/journal.pone.0124049 es_ES
dc.description.upvformatpinicio 21 es_ES
dc.description.upvformatpfin 53 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 5 es_ES
dc.relation.senia 290279 es_ES
dc.identifier.pmid 25961305 en_EN
dc.identifier.pmcid PMC4427504
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.description.references Licoppe, C., & Smoreda, Z. (2005). Are social networks technologically embedded? Social Networks, 27(4), 317-335. doi:10.1016/j.socnet.2004.11.001 es_ES
dc.description.references European Commission. JRC Scientific and Policy Report EUR 25295 EN ‘Pan-European Survey of Practices, Attitudes and Policy Preferences as regards Personal Identity Data Management’; 2012. http://is.jrc.ec.europa.eu/pages/TFS/documents/EIDSURVEY_Web_001.pdf Last access: 27-01-2015. es_ES
dc.description.references Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169-2188. doi:10.1002/asi.21149 es_ES
dc.description.references Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007). The dynamics of viral marketing. ACM Transactions on the Web, 1(1), 5-es. doi:10.1145/1232722.1232727 es_ES
dc.description.references Guimerà, R., Llorente, A., Moro, E., & Sales-Pardo, M. (2012). Predicting Human Preferences Using the Block Structure of Complex Social Networks. PLoS ONE, 7(9), e44620. doi:10.1371/journal.pone.0044620 es_ES
dc.description.references Borondo J, Morales AJ, Losada JC, Benito RM. Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish Presidential Election as a case study.; 2013. es_ES
dc.description.references Ahn YY, Han S, Kwak H, Moon S, Jeong H. Analysis of Topological Characteristics of Huge Online Social Networking Services. In: Proceedings of the 16th International Conference on World Wide Web. WWW ‘07. New York, NY, USA: ACM; 2007. p. 835–844. es_ES
dc.description.references Romero DM, Galuba W, Asur S, Huberman BA. Influence and Passivity in Social Media. In: Proceedings of the 20th International Conference Companion on World Wide Web. WWW ‘11. New York, NY, USA: ACM; 2011. p. 113–114. es_ES
dc.description.references Romero DM, Meeder B, Kleinberg J. Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter. In: Proceedings of the 20th International Conference on World Wide Web. WWW ‘11. New York, NY, USA: ACM; 2011. p. 695–704. es_ES
dc.description.references Bastiaensens, S., Vandebosch, H., Poels, K., Van Cleemput, K., DeSmet, A., & De Bourdeaudhuij, I. (2014). Cyberbullying on social network sites. An experimental study into bystanders’ behavioural intentions to help the victim or reinforce the bully. Computers in Human Behavior, 31, 259-271. doi:10.1016/j.chb.2013.10.036 es_ES
dc.description.references Google. Google Co-op; 2014. [Access 5-Jan-2014]. http://www.google.com/coop/ es_ES
dc.description.references Wasserman, S., & Faust, K. (1994). Social Network Analysis. doi:10.1017/cbo9780511815478 es_ES
dc.description.references Kossinets, G. (2006). Empirical Analysis of an Evolving Social Network. Science, 311(5757), 88-90. doi:10.1126/science.1116869 es_ES
dc.description.references Scott, J. (2010). Social network analysis: developments, advances, and prospects. Social Network Analysis and Mining, 1(1), 21-26. doi:10.1007/s13278-010-0012-6 es_ES
dc.description.references Newman, M. (2010). Networks. doi:10.1093/acprof:oso/9780199206650.001.0001 es_ES
dc.description.references Huberman BA, Romero DM, Wu F. Social networks that matter: Twitter under the microscope. arXiv preprint arXiv:08121045. 2008;. es_ES
dc.description.references Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B. Measurement and Analysis of Online Social Networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. IMC ‘07. New York, NY, USA: ACM; 2007. p. 29–42. es_ES
dc.description.references Borge-Holthoefer, J., Rivero, A., García, I., Cauhé, E., Ferrer, A., Ferrer, D., … Moreno, Y. (2011). Structural and Dynamical Patterns on Online Social Networks: The Spanish May 15th Movement as a Case Study. PLoS ONE, 6(8), e23883. doi:10.1371/journal.pone.0023883 es_ES
dc.description.references na López IP, Congosto M, Aragón P. Spanish Indignados and the evolution of 15M: towards networked para-institutions. Big Data: Challenges and Opportunities. 2013;p. 359–386. es_ES
dc.description.references Cha M, Haddadi H, Benevenuto F, Gummadi KP. Measuring user influence in Twitter: The million follower fallacy. In: in ICWSM ‘10: Proceedings of international AAAI Conference on Weblogs and Social; 2010. es_ES
dc.description.references Shamma DA, Kennedy L, Churchill EF. Tweet the Debates: Understanding Community Annotation of Uncollected Sources. In: Proceedings of the First SIGMM Workshop on Social Media. WSM ‘09. New York, NY, USA: ACM; 2009. p. 3–10. Available from: http://doi.acm.org/10.1145/1631144.1631148 es_ES
dc.description.references Diakopoulos NA, Shamma DA. Characterizing Debate Performance via Aggregated Twitter Sentiment. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI ‘10. ACM; 2010. p. 1195–1198. es_ES


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