Big y Small data: ver y discutir series de televisión en streaming
DOI:
https://doi.org/10.7764/cdi.49.27297Palabras clave:
Big data, microanálisis, series de televisión, discurso, Netflix, opacidad, comunidades de fansResumen
Este trabajo analiza los procesos de comunicación en medios en línea sobre cuatro series de televisión, todas ellas en Netflix (La Casa de Papel, Peaky Blinders, Elite y Educación Sexual). Nos interesa cómo estas series de streaming son reconstruidas por audiencias específicas en las redes sociales. Nos fijamos en las conversaciones organizadas alrededor de estas series por parte de comunidades digitales de fans en Twitter, Facebook, YouTube, y otros contextos digitales (por ejemplo, las revistas en línea). Se generó un total de 408.536 menciones y 189.040 usuarios como corpus de datos. El proceso de análisis requiere de una canalización del flujo de datos, definiendo un sistema coherente de temas y categorías. El artículo muestra los elementos presentes en las comunidades de fans para reconstruir las historias, por ejemplo, mencionando a actores o a personajes. Aparecen dimensiones muy similares tanto en relación con las historias como con la forma en la que se construyen a partir de una situación pandémica.
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Adams, N. (2019, June 12). No more tradeoffs: The era of big data content analysis has come. Retrieved from https://ocean.sagepub.com/blog/no-more-tradeoffs-the-era-of-big-data-content-analysis-has-come
Ananny, M. & Crawford, K. (2016). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973-989. https://doi.org/10.1177/1461444816676645
Andersen, T. R. & Linkis, S. T. (2019). As We Speak: Concurrent Narration and Participation in the Serial Narratives "@I_Bombadil" and Skam. Narrative, 27(1), 83-106. https://doi.org/https://doi.org/10.1353/nar.2019.0005
Arrojo, M. J. & Martin, E. (2019). El seguimiento activo de las series de ficción en internet. La atención y la emoción como desencadenantes del binge-watching (Active following (engagement) of fiction series on the internet. Attention and emotion as triggers of the binge-watching). Revista De Comunicación, 18(2), 3-23. https://doi.org/10.26441/rc18.2-2019-a1-1
Barocas, S. & Selbst, A. D. (2016). big data's Disparate Impact. California Law Review, 104(3), 671-732. Retrieved from www.jstor.org/stable/24758720
Bednarek, M. & Caple, H. (2017). The discourse of news value: how news organizations create newsworthiness. Oxford, United Kingdom: Oxford University Press.
Bell, G., Boellstorff, T., Gregg, M., Maurer, B., & Seaver, N. (Eds.). (2015). Data, now bigger and better! Chicago, IL: Prickly Paradigm Press.
Bollier, D. (2010). The Promise and Peril of big data. Washington, DC: The Aspen Institute.
boyd, d. & Crawford, K. (2012, 2012/06/01). Critical questions for big data. Information, Communication & Society, 15(5), 662-679. https://doi.org/10.1080/1369118X.2012.678878
Brown, D. M., Soto-Corominas, A., Suárez, J. L., & Rosa, J. d. l. (2017). Overview – The Social Media Data Processing Pipeline. In L. Sloan & A. Quan-Haase (Eds.), The SAGE handbook of social media research methods (pp. 125-145). Sage.
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512. https://doi.org/10.1177/2053951715622512
Christin, A. (2020a). The ethnographer and the algorithm: beyond the black box. Theory and Society, 49, 897-918. https://doi.org/10.1007/s11186-020-09411-3
Christin, A. (2020b). Metrics at work: journalism and the contested meaning of algorithms. Princeton, NJ: Princeton University Press.
Christin, A. (2020c). What Data Can Do: A Typology of Mechanisms. International Journal of Communication, 14, 1115-1134. Retrieved from https://ijoc.org/index.php/ijoc/article/view/12220/2980
Corta, S. (2019). El imparable crecimiento de la producción de series en España (The inexorable growth of series production in Spain). Observatori de la Producció Audiovisual (OPA). Retrieved from https://repositori.upf.edu/handle/10230/36682
Crawford, K., Gray, M. L., & Miltner, K. (2014). Big Data| Critiquing big data: Politics, Ethics, Epistemology | Special Section Introduction. International Journal of Communication, 8, 10. Retrieved from http://ijoc.org/index.php/ijoc/article/view/2167
Creeber, G. (2004). Serial television: big drama on the small screen. BFI Publishing. Diakopoulos, N. (2015). Algorithmic Accountability. Digital Journalism, 3(3), 398-415. https://doi.org/10.1080/21670811.2014.976411
Dijck, J. v., Poell, T., & Waal, M. d. (2018). The platform society. Oxford, United Kingdom: Oxford University Press.
Dourish, P. & Gómez Cruz, E. (2018, 2018/07/01). Datafication and data fiction: Narrating data and narrating with data. Big Data & Society, 5(2), 2053951718784083. https://doi.org/10.1177/2053951718784083
Duffett, M. (2013). Understanding fandom: an introduction to the study of media fan culture. Bloomsbury.
Eubanks, V. (2018). Automating inequality: how high-tech tools profile, police, and punish the poor. St. Martin's Press.
Fernández-Manzano, E. P., Neira, E., Clares-Gavilán, J. (2016). Gestión de datos en el negocio audiovisual: Netflix como estudio de caso (Data management in audiovisual business: Netflix as a case study). Profesional de la Información, 25(4), 568-577. https://doi.org/10.3145/epi.2016.jul.06
Gauntlett, D. (2009). Media studies 2.0: A response. Interactions: Studies in Communication & Culture, 1(1), 147-157. https://doi.org/10.1386/iscc.1.1.147_1
Gee, J. P. (2014). An introduction to discourse analysis: theory and method (4th ed.). Routledge.
Gitelman, L. (2013). "Raw data" is an oxymoron. Cambridge, MS: The MIT Press.
Gomez-Uribe, C. & Hunt, N. (2016). The Netflix recommender system. ACM Transactions o Management Information Systems, 6(4), 1-19. https://doi.org/10.1145/2843948
Govind, N. (2014, June 11). Optimizing the Netflix Streaming Experience with Data Science. Retrieved from http://techblog.netflix.com/2014/06/optimizing-netflix-streaming-experience.html
Halpern, D., Quintas-Froufe, N., & Fernández-Medina, F. (2016). Interacciones entre la televisión y su audiencia social: hacia una conceptualización comunicacional (Interactions between television and its social audience: Towards a communication conceptualization). El Profesional de la Información, 25(3), 367-375. https://doi.org/10.3145/epi.2016.may.06
Harrigan, P., Kirschenbaum, M. G., & Dunnigan, J. F. (2016). Zones of control: perspectives on wargaming. Cambridge, MS: The MIT Press.
Heredia-Ruiz, V., Quirós-Ramírez, A. C., & Quiceno-Castañeda, B. E. (2021). Netflix: catálogo de contenido y flujo televisivo en tiempos de big data (Netflix: content catalog and television flow in times of big data). Revista De Comunicación, 20(1), 117-136. https://doi.org/10.26441/RC20.1-2021-A7
Iqbal, M. (2020, April 24). Netflix Revenue and Usage Statistics (2020). Retrieved from https://www.businessofapps.com/data/netflix-statistics/#4
Jenkins, H. (2013). Textual poachers: television fans and participatory culture (Updated 20th anniversary ed.). Routledge.
Jenner, M. (2015). Binge-watching: Video-on-demand, quality TV and mainstreaming fandom. International Journal of Cultural Studies, 20(3), 304-320. https://doi.org/10.1177/1367877915606485
Jenner, M. (2018). Netflix and the re-invention of television. Palgrave Macmillan.
Jongbloed, E. U. (2016). El cambio mediático de la televisión: Netflix y la televisión en teléfonos inteligentes (The Media Change in Television: Netflix and Television in Smartphones). Palabra Clave, 19(2), 358-364. https://doi.org/10.5294/pacla.2016.19.2.1
Karlsson, M. & Strömbäck, J. (2010). Freezing the flow of online news. Journalism Studies, 11(1), 2-19. https://doi.org/10.1080/14616700903119784
Karpf, D. (2012). Social science research methods in Internet time. Information, Communication & Society, 15(5), 639-661. https://doi.org/10.1080/1369118X.2012.665468
Kirschenbaum, M. G. (2008). Mechanisms: new media and the forensic imagination. Cambridge, MS: MIT Press.
Kitchin, R. (2014a). Big data, new epistemologies and paradigm shifts. Big data& Society 1(1), 2053951714528481. https://doi.org/10.1177/2053951714528481
Kitchin, R. (2014b). The data revolution: big data, open data, data infrastructures & their consequences. Sage Publications Ltd.
Krüger, S. & Rustad, G. C. (2017). Coping with Shame in a Media-saturated Society: Norwegian Web-series Skam as Transitional Object. Television & New Media, 20(1), 72-95. https://doi.org/10.1177/1527476417741379
Lacasa, P. (2020). Adolescents fans. Practices, discourses, and communities. Peter Lang.
Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content Analysis in an Era of big data: A Hybrid Approach to Computational and Manual Methods. Journal of Broadcasting & Electronic Media, 57(1), 34-52. https://doi.org/10.1080/08838151.2012.761702
Manovich, L. (2011). What is visualization? Visual Studies, 26(1), 36-49. https://doi.org/10.1080/1472586X.2011.548488
Markham, A. N. (2013, 09/21). Undermining ‘data’: A critical examination of a core term in scientific inquiry. First Monday, 18(10). https://doi.org/10.5210/fm.v18i10.4868
Martín, A. y. M.-L., M. (2016). Los nuevos amos de la televisión (The new masters of televisión). El Independiente. https://doi.org/10.3145/epi.2019.mar.04
McMillan, S. J. (2000). The Microscope and the Moving Target: The Challenge of Applying Content Analysis to the World Wide Web. Journalism & Mass Communication Quarterly, 77(1), 80-98. https://doi.org/10.1177/107769900007700107
Morgan, D. L. (2018). Skinner, Gibson, and Embodied Robots: Challenging the Orthodoxy of the Impoverished Stimulus. Journal of Theoretical and Philosophical Psychology, 38(3), 140-153. https://doi.org/10.1037/teo0000083
Netflix. (2013, June 6). Netflix quick guide: How does Netflix decide what’s on Netflix (video file). YouTube. Retrieved from https://www.youtube.com/watch?v=VvpoUh9gx58
Pasquale, F. (2015). The black box society: the secret algorithms that control money and information. Harvard, MS: Harvard University Press.
Paz Rebollo, M. A. & Montero Díaz, J. (2017, 2017/01/02). La programación juvenil de Televisión Española (1982–1989): una oportunidad perdida (The youth programming of Spanish televisión - 1982-1989): a lost opportunity. Journal of Spanish Cultural Studies, 18(1), 21-36. https://doi.org/10.1080/14636204.2016.1274499
Pink, S. (2016). Conferencia Magistral | big data y Etnografía: datos personales en un mundo incierto (Keynote Lecture | big data and Ethnography: personal data in an uncertain world) (video file). Pontificia Universidad Católica del Perú. Retrieved from https://educast.pucp.edu.pe/video/6841/conferencia_magistral__big_data_y_etnografia_datos_personales_en_un_mundo_incierto
Pink, S., Ruckenstein, M., Willim, R., & Duque, M. (2018). Broken data: Conceptualising data in an emerging world. Big Data & Society, 5(1), 2053951717753228. https://doi.org/10.1177/2053951717753228
Riffe, D., Lacy, S., & Fico, F. (2014). Analyzing media messages: using quantitative content analysis in research (3d ed.). Routledge.
Siles, I. E.-R., J., Naranjo, A., & Tristán, M. F. (2019). The Mutual Domestication of Users and Algorithmic Recommendations on Netflix. Communication, Culture & Critique 12(4), 499-518. https://doi.org/10.1093/ccc/tcz025
Sjøvaag, H. & Stavelin, E. (2012). Web media and the quantitative content analysis: Methodological challenges in measuring online news content. Convergence, 18(2), 215-229. https://doi.org/10.1177/1354856511429641
Zuboff, S. (2015). Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1), 75-89. https://doi.org/10.1057/jit.2015.5
Zuboff, S. (2019). The age of surveillance capitalism: the fight for a human future at the new frontier of power. PublicAffairs.
