Polarización en Twitter durante la crisis de la COVID-19Caso Aislado y Periodista Digital

  1. Arce García, Sergio 1
  2. Vila Márquez, Fátima 2
  3. Fondevila i Gascón, Joan Francesc 3
  1. 1 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

  2. 2 Universitat de Barcelona
    info

    Universitat de Barcelona

    Barcelona, España

    ROR https://ror.org/021018s57

  3. 3 Blanquerna-Universitat Ramon Llull
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Revista:
Revista de comunicación

ISSN: 1684-0933,  2227-1465

Año de publicación: 2021

Volumen: 20

Número: 2

Páginas: 29-47

Tipo: Artículo

DOI: 10.26441/RC20.2-2021-A2 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Revista de comunicación

Resumen

The announcement of the State of Alarm in Spain in March 2020 brought with it a period of great information intensity in traditional and digital media. The extraordinary nature of the measure, which provided the Government with exceptional measures to confront the Covid-19 pandemic, gave rise to a tremendously polarized scenario. In this context, some webs known for the dissemination of disinformation campaigns and, even, the promotion of ideas closes to the alt-right, were especially active in networks promoting the dissemination of ideological content with the aim of capturing traffic for subsequent monetization through advertising. This work follows the activity around of two of these webs on Twitter, Caso Aislado and Periodista Digital, with the intention of determinate their role in the political polarization. For more than two months, more than 100,000 tweets were captured, stored and studied using R software and various analysis algorithms to determine their social activity, the possible presence or not of bots or automated profiles, the nature of the content and the emotional charge associated with it. There is an intense organized activity around both portals through a high percentage of apparently automated accounts and the support of influencers profiles. Although with differences inherent around each web, it is possible to glimpse an intentional coordination through campaigns that combine content, use of support accounts and automations.

Referencias bibliográficas

  • Ahmed, W., Vidal-Alaball, J., Downing, J. y López Seguí, F. (2020). COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data. Journal of Medical Internet Research, 22(5), e19458. https://doi.org/10.2196/19458
  • Arce García, S., Orviz Martínez, N. y Cuervo Carabel, T. (2020). Impacto de las emociones vertidas por diarios digitales españoles. El Profesional de la Información, 29(5).
  • Asociación de la Prensa de Madrid (27 de julio de 2014). Periodistadigital.com y su director vulneraron el código deontológico en una noticia sobre una mujer víctima de secuestro y violación. Federación de Asociaciones de Periodistas de España (FAPE). https://bit.ly/3iQgj0M
  • Auxier, B.E. y Vitak, J. (2019). Factors Motivating Customization and Echo Chamber Creation Within Digital News Environments. Social Media + Society, 5(2), 205630511984750. https://doi.org/10.1177/2056305119847506
  • Bakir, V. y Mcstay, A. (2017). Fake News and The Economy of Emotions. Digital Journalism, 6(2), 154-175.
  • Bastian, M.; Heymann, S. y Jacomy, M. (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. En Proceedings of the Third International ICWSM Conference, 17-20 de mayo, San Jose: California.
  • Becerra, M. (2016). Revolución digital: una introducción. Entre la crisis y sostenibilidad. Revista Mexicana de Comunicación, 1(139), 64-69.
  • Bell, E. J., Owen, T., Brown, P.D., Hauka, C. y Rashidian, N. (2017). The Platform Press: How Silicon Valley Reengineered Journalism. Tow Center, Columbia Journalism School. https://bit.ly/3l1484a
  • Blondel, V., Guillaume, J., Lambiotte, R. y Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10. https://doi.org/10.1088/1742-5468/2008/10/P10008
  • Calvo, E., Aruguete, N. (2020). Fake news, trolls y otros encantos. Cómo funcionan (para bien y para mal) las redes sociales. Siglo XXI Editores.Campos Freire, F. (2008). Las redes sociales trastocan los modelos de los medios de comunicación tradicionales. Revista Latina de Comunicación Social, 63, 287-293. https://doi.org/10.4185/RLCS-63-2008-767-287-293
  • Cid, G. (05 de mayo, 2020). 1M de clics al mes por cabrearte: las webs de desinformación se disparan con el covid. El Confidencial. https://bit.ly/375saT8
  • Comisión Europea (2018). A multi-dimensional approach to disinformation. Report of the independent high level group on fake news and online disinformation. Luxembourg: Publications Office of the European Union.
  • Evolvi, G. (2017). #Islamexit: inter-group antagonism on Twitter. Information, Communication & Society, 22(3), 386-401. https://doi.org/10.1080/1369118x.2017.1388427
  • Freelon, D., Bossetta, M., Wells, C., Lukito, J., Xia, Y. y Adams, K. (2020). Black Trolls Matter: Racial and Ideological Asymmetries in Social Media Disinformation. Social Science Computer Review, 089443932091485. https://doi.org/10.1177/0894439320914853
  • Frischlisch, L., Klapproth, J. y Brinkschulte, F. (2019). Between Mainstream and Alternative – Co-orientation in Right-Wing Populist Alternative News Media. En First Multidisciplinary International Symposium, MISDOOM 2019. Hamburg, Germany, February 27 – March 1, 2019. https://link.springer.com/book/10.1007/978-3-030-39627-5
  • Glenski, M., Weninger, T. y Volkova, S. (2018) Propagation From Deceptive News Sources Who Shares, How Much, How Evenly, and How Quickly?. IEEE Transactions on Computational Social Systems, 5(4), 1071-1082. https://doi.org/10.1109/TCSS.2018.2881071
  • Goyanes Martínez, M. (2012). Monetizar el periodismo digital. La hoja de ruta en la que el lector es el eslabón fundamental. Razón y Palabra, 81. http://www.razonypalabra.org.mx/N/N81/V81/28_Goyanes_V81.pdf
  • Grimme, C., Preuss, M., Takes, F.W. y Waldherr, A. (2019). Disinformation in Open Online Media. En First Multidisciplinary International Symposium, MISDOOM 2019. Hamburg, Germany, February 27 – March 1, 2019. https://link.springer.com/book/10.1007/978-3-030-39627-5
  • Gutiérrez Martín, A., Torrego González, A. y Vicente Mariño, M. (2019). Media education with the monetization of YouTube: the loss of truth as an exchange value / Educación mediática frente a la monetización en YouTube: la pérdida de la verdad como valor de cambio. Cultura y Educación, 31(2), 267-295. https://doi.org/10.1080/11356405.2019.1597443
  • Hernández Conde, M. y Fernández García, M. (2019). Partidos emergentes de la ultraderecha: ¿fake news, fake outsiders? Vox y la web Caso Aislado en las elecciones andaluzas de 2018. Teknokultura. Revista de Cultura Digital y Movimientos Sociales, 16(1), 33-53.
  • Holbrook, E., Kaur, G., Bond, J., Imbriani, J., Nsoesie, E., y Grant, C. (2016). Tweet Geolocation Error Estimation.EnInternational Conference on GIScience Short Paper Proceedings, 1.Montreal, Canada, 27 – 30 Septiembre, 2016. https://doi.org/10.21433/b3110wf6w9p9
  • Hu, Y. (2006). Efficient, High-Quality Force-Directed Graph Drawing. The Mathematica Journal, 10(1), 37-71. https://cutt.ly/VyDIfpR
  • Iyengar, S., Hahn K. S. (2009). Red media, blue media: evidence of ideological selectivity in media use. Journal of Communication, 59(1), 19-39.
  • Jockers, M. (2017). Syuzhet, extracts sentiment and sentiment-derived plot arcs from text. https://www.rdocumentation.org/packages/syuzhet/versions/1.0.4
  • Kawchuk, G., Hartvigsen, J., Harsted, S., Glissmann Nim, C. y Nyirö, L. (2020). Misinformation about spinal manipulation and boosting immunity: an analysis of Twitter activity during the COVID-19 crisis. Chiropractic Manual Therapies, 28 (34). https://doi.org/10.1186/s12998-020-00319-4
  • Kearney, M.W. (2018). Tweetbotornot: An R package for classifying Twitter accounts as bot or not. https://github.com/mkearney/tweetbotornot
  • Kearney, M.W. (2019). Rtweet: Collecting and analyzing Twitter data. Journal of Open Source Software, 4(42), 1829. https://doi.org/10.21105/joss.01829
  • Kessling, P. y Grimme, C. (2019). Analysis of Account Engagement in Onsetting Twitter Message Cascades. En First Multidisciplinary International Symposium, MISDOOM 2019. Hamburg, Germany, Febrero 27 – Marzo 1, 2019. https://link.springer.com/book/10.1007/978-3-030-39627-5
  • Kilgo, D. K, Yoo, J. y Johnson, T. J. (2019). Spreading Ebola Panic: Newspaper and Social Media Coverage of the 2014 Ebola Health Crisis. Health Communication, 34(8), 811-817. https://doi.org/10.1080/10410236.2018.1437524
  • Klinger, U. y Svensson, J. (2015). The emergence of network media logic in political communication: A theoretical approach. New Media & Society, 17(8), 1.241-1.257. https://doi.org/10.1177/1461444814522952
  • Levi, S. (2019). #FakeYou, fake news y desinformación. Barcelona, España: Rayo Verde Ed.
  • Lopez Pan, F. y Rodríguez Rodríguez, J.M. (2020). El Fact Checking en España. Plataformas, prácticas y rasgos distintivos. Estudios Sobre El Mensaje Periodístico, 26(3), 1045-1065. https://doi.org/10.5209/esmp.65246
  • Martin, S., Brown, W., Klavans, R. y Boyack, K. (2011). OpenOrd: An Open-Source Toolbox for Large Graph Layout. En Proc. SPIE, Visualization and Data Analysis 2011, 7868. https://doi.org/10.1117/12.871402.
  • Meel, P. y Vishwakarma, D. K. (2019). Fake News, Rumor, Information Pollution in Social Media and Web: A Contemporary Survey of State-of-the-arts, Challenges and Opportunities. Expert Systems with Applications, 112986. https://doi.org/10.1016/j.eswa.2019.112986
  • Mohammad, S. y Turney, P. (2010). Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon. En Proceedings of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. June 2010. LA: California.
  • Mohammad, S. y Turney, P. (2013). Crowdsourcing a Word-Emotion Association Lexicon. Computational Intelligence, 29(3), 436-465. https://doi.org/10.1111/j.1467-8640.2012.00460.x
  • Mourão, R.R. y Robertson, C.T. (2019): Fake News as Discursive Integration: An Analysis of Sites That Publish False, Misleading, Hyperpartisan and Sensational Information. Journalism Studies, 20(14), 2077-2095. https://doi.org/10.1080/1461670X.2019.1566871
  • Murolo, N.L. (2012). Nuevas pantallas: un desarrollo conceptual. Razón y Palabra, 16(1_80), 555-565.
  • Nielsen, R. K. y Ganter S. A. (2017). Dealing with Digital Intermediaries: A Case Study of the Relations between Publishers and Platforms. New Media & Society, 20(4), 1600-1617. https://doi.org/10.1177/1461444817701318
  • Oltmann, S.M., Cooper, T.B. y Proferes, N. (2020). How Twitter’s affordances empower dissent and information dissemination: An exploratory study of the rogue and alt government agency Twitter accounts. Government Information Quarterly, 37(3), 101475. https://doi.org/10.1016/j.giq.2020.101475
  • Padilla Herrada, M.-S. (2016). Marcadores y partículas discursivas interactivas en el entorno político/periodístico de Twitter. Philologia Hispalensis, Revista de Estudios Lingüisticos y Literarios, 30(1), 193-212. https://doi.org/10.12795/PH.2016.i30.10
  • Pariser, E. (2011). The Filter Bubble. Londres, Reino Unido: Penguin Books.
  • Parra-Valero, P., Rubio-Jordán, A.-V. (2020). Utilización de prensa nativa digital en las universidades españolas: causas de su reducida presencia. Profesional de la información, 29(5), e290526. https://doi.org/10.3145/epi.2020.sep.26
  • Peinado, F. y Muela, D. (2018, 23 de mayo). El negocio de la manipulación digital en España. El País. https://bit.ly/2URouly
  • Peterson, T. (2018). The New York Times has folded its programmatic sales team into its larger ad sales org. Digital Day. https://digiday.com/media/new-york-times-folded-programmatic-sales-team-larger-ad-sales-org/
  • Ramírez, V. y Castellón, J. (2018). ‘Caso Aislado’, el fabricante español de ‘fake news’ vinculado a VOX. La Sexta. https://bit.ly/3f1aMDA
  • Rosenberg, H., Syed, S. y Rezaie, S. (2020). The twitter pandemic: The critical role of twitter in the dissemination of medical information and misinformation during the COVID-19 Pandemic. Canadian Journal of Emergency Medicine, 22(4), 418-421. https://doi.org/10.1017/cem.2020.361
  • Salaverría, R., Buslón, N., López-Pan, F., León, B., López-Goñi, I. y Erviti, M.-C. (2020). Desinformación en tiempos de pandemia: tipología de los bulos sobre la Covid-19. El profesional de la información, 29(3), e290315. https://doi.org/10.3145/epi.2020.may.15
  • Salaverría, R., Martínez-Costa, M.P., Breiner, J.G., Negredo Bruna, S., Negreira Rey, M.C., Jimeno, M.A. (2019). El mapa de los cibermedios en España. En Toural-Bran, C. López-García, X. (Eds.), Ecosistema de los cibermedios en España: tipologías, iniciativas, tendencias narrativas y desafíos.Salamanca: Comunicación Social Ediciones y Publicaciones. https://doi.org/10.52495/c1.emcs.3.p73
  • Sarabia, D. (28 de octubre de 2019). Los periodistas ‘fake’ de Periodista Digital: identidad falsa, foto sacada de Internet y currículum inventado. Eldiario.es. https://bit.ly/3zDB86h
  • Schulz, A. (2018). Where populist citizens get the news: An investigation of news audience polarization along populist attitudes in 11 countries. Communication Monographs, 86(1), 88-111. https://doi.org/10.1080/03637751.2018.1508876
  • Sell, T.K., Hosangadi, D. y Trtochaud, M. (2020). Misinformation and the US Ebola communication crisis: analyzing the veracity and content of social media messages related to a fear-inducing infectious disease outbreak. BMC Public Health, 20, 550. https://doi.org/10.1186/s12889-020-08697-3
  • Spohr, D. (2017). Fake news and ideological polarization. Business Information Review, 34(3), 150–160. https://doi.org/10.1177/0266382117722446
  • Sundar, S. S. (2008). The MAIN Model: A Heuristic Approach to Understanding Technology Effects on Credibility. En Metzger M. J. y Flanagin, A. J (Eds.),Digital Media, Youth, and Credibility, 73-100. Cambridge, MA: The MIT Press.
  • Tandoc, E. C., Lim, Z. W. y Ling, R. (2017). Defining “Fake News”. Digital Journalism, 6(2), 137–153.
  • Urman, A. (2019). Context matters: political polarization on Twitter from a comparative perspective. Media, Culture & Society, 016344371987654. https://doi.org/10.1177/0163443719876541
  • Van der Veen, H., Hiemstra, D., Van den Broek, T., Ehrenhard, M. y Need, A. (2015). Determine the User Country of a Tweet. Social and Information Networks. https://arxiv.org/abs/1508.02483
  • Vila Márquez, F. y Arce García, S. (2019). Fake News y difusión en Twitter: el caso de Curro, el perro “condenado”. Historia y Comunicación Social, 24(2), 485-503. https://doi.org/10.5209/hics.66292
  • Vraga, E. K.;, Bode, L. y Tully, M. (2020). Creating News Literacy Messages to Enhance Expert Corrections of Misinformation on Twitter. Communication Research, 009365021989809. https://doi.org/10.1177/0093650219898094
  • Walter, D., Ophir, Y. y Jamieson, K. H. (2020). Russian Twitter Accounts and the Partisan Polarization of Vaccine Discourse, 2015–2017. American Journal of Public Health, 110, 718-724. https://doi.org/10.2105/ajph.2019.305564
  • Wissman, B. (2 de marzo 2018). Micro-Influencers: The Marketing Force of The Future?. Forbes. https://bit.ly/3iV9xXN.
  • Xu, Q., Chen, S. y Safarnejad, L. (2020): Effects of Information Veracity and Message Frames on Information Dissemination: A Case Study of 2016 Zika Epidemic Discussion on Twitter. Health Communication. https://doi.org/10.1080/10410236.2020.1773705
  • Zannettou, S., Sirivianos, M., Blackburn, J. y Kourtellis, N. (2019). The Web of False Information. Journal of Data and Information Quality, 11(3), 1–37. https://doi.org/10.1145/3309699
  • Zola, P., Ragno, C. y Cortez, P. (2020). A Google Trends spatial clustering approach for a worldwide Twitter user geolocation. Information Processing & Management, 57(6), 102312.https://doi.org/10.1016/j.ipm.2020.102312
Fuente de los datos: Dialnet