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A Twitter Political Corpus of the 2019 10N Spanish Election

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A Twitter Political Corpus of the 2019 10N Spanish Election

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dc.contributor.author Sánchez-Junquera, Javier es_ES
dc.contributor.author Ponzetto, Simone Paolo es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.date.accessioned 2022-01-18T08:11:49Z
dc.date.available 2022-01-18T08:11:49Z
dc.date.issued 2020-09-11 es_ES
dc.identifier.isbn 978-3-030-58323-1 es_ES
dc.identifier.uri http://hdl.handle.net/10251/179804
dc.description.abstract [EN] We present a corpus of Spanish tweets of 15 Twitter accounts of politicians of the main five parties (PSOE, PP, Cs, UP and VOX) covering the campaign of the Spanish election of 10th November 2019 (10N Spanish Election). We perform a semi-automatic annotation of domainspecific topics using a mixture of keyword-based and supervised techniques. In this preliminary study we extracted the tweets of few politicians of each party with the aim to analyse their official communication strategy. Moreover, we analyse sentiments and emotions employed in the tweets. Although the limited size of the Twitter corpus due to the very short time span, we hope to provide with some first insights on the communication dynamics of social network accounts of these five Spanish political parties. es_ES
dc.description.sponsorship The work of the authors from the Universitat Politecnica de Valencia was funded by the Spanish MICINN under the research project MISMISFAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof Text, Speech, and Dialogue. 23rd International Conference, TSD 2020 es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;12284 es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Twitter es_ES
dc.subject Political text analysis es_ES
dc.subject Topic detection es_ES
dc.subject Sentiment and emotion analysis es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A Twitter Political Corpus of the 2019 10N Spanish Election es_ES
dc.type Comunicación en congreso es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-58323-1_4 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-096212-B-C31/ES/DESINFORMACION Y AGRESIVIDAD EN SOCIAL MEDIA: AGREGANDO INFORMACION Y ANALIZANDO EL LENGUAJE/ 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 Sánchez-Junquera, J.; Ponzetto, SP.; Rosso, P. (2020). A Twitter Political Corpus of the 2019 10N Spanish Election. Springer. 41-49. https://doi.org/10.1007/978-3-030-58323-1_4 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 23rd International Conference on Text, Speech and Dialogue (TSD 2020) es_ES
dc.relation.conferencedate Septiembre 08-11,2020 es_ES
dc.relation.conferenceplace Online es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-58323-1_4 es_ES
dc.description.upvformatpinicio 41 es_ES
dc.description.upvformatpfin 49 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\419272 es_ES
dc.description.references Abercrombie, G., Nanni, F., Batista-Navarro, R., Ponzetto, S.P.: Policy preference detection in parliamentary debate motions. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Hong Kong, China, pp. 249–259. Association for Computational Linguistics, November 2019 es_ES
dc.description.references Ekman, P., et al.: Universals and cultural differences in the judgments of facial expressions of emotion. J. Pers. Soc. Psychol. 53(4), 712 (1987) es_ES
dc.description.references Gao, W., Sebastiani, F.: Tweet sentiment: from classification to quantification. In: 2015 IEEE/ACM International Conference on ASONAM, pp. 97–104. IEEE (2015) es_ES
dc.description.references Glavaš, G., Nanni, F., Ponzetto, S.P.: Computational analysis of political texts: bridging research efforts across communities. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, Florence, Italy, pp. 18–23. Association for Computational Linguistics, July 2019 es_ES
dc.description.references Kornilova, A., Argyle, D., Eidelman, V.: Party matters: enhancing legislative embeddings with author attributes for vote prediction, pp. 510–515. Association for Computational Linguistics, July 2018 es_ES
dc.description.references Lowe, W., Benoit, K., Mikhaylov, S., Laver, M.: Scaling policy preferences from coded political texts. Legis. Stud. Q. 36(1), 123–155 (2011) es_ES
dc.description.references Marchetti-Bowick, M., Chambers, N.: Learning for microblogs with distant supervision: political forecasting with Twitter. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, Avignon, France, pp. 603–612. Association for Computational Linguistics, April 2012 es_ES
dc.description.references Menini, S., Nanni, F., Ponzetto, S.P., Tonelli, S.: Topic-based agreement and disagreement in US electoral manifestos. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp. 2938–2944. Association for Computational Linguistics, September 2017 es_ES
dc.description.references Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., Stoyanov, V.: Semeval-2016 task 4: sentiment analysis in Twitter. arXiv preprint arXiv:1912.01973 (2019) es_ES
dc.description.references Nanni, F., et al.: Findings from the hackathon on understanding euroscepticism through the lens of textual data. European Language Resources Association (ELRA), May 2018 es_ES
dc.description.references O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010, Washington, DC, USA, 23–26 May 2010 (2010) es_ES
dc.description.references Rheault, L., Cochrane, C.: Word embeddings for the analysis of ideological placement in parliamentary corpora. Polit. Anal. 1–22 (2019) es_ES
dc.description.references Roberts, M.E., et al.: Structural topic models for open-ended survey responses. Am. J. Polit. Sci. 58(4), 1064–1082 (2014) es_ES
dc.description.references Sidorov, G., et al.: Empirical study of machine learning based approach for opinion mining in tweets. In: Batyrshin, I., González Mendoza, M. (eds.) MICAI 2012. LNCS (LNAI), vol. 7629, pp. 1–14. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37807-2_1 es_ES
dc.description.references Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inform. Sci. Technol. 61(12), 2544–2558 (2010) es_ES
dc.description.references Thomas, M., Pang, B., Lee, L.: Get out the vote: determining support or opposition from congressional floor-debate transcripts. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 327–335. Association for Computational Linguistics, July 2006 es_ES


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