Cuando la negatividad es el combustible. Bots y polarización política en el debate sobre el COVID-19

  1. José Manuel Robles
  2. Juan Antonio Guevara Gil
  3. Belén Casas Mas
  4. Daniel Gómez González
Revista:
Comunicar: Revista Científica de Comunicación y Educación
  1. Cáceres Zapatero, María Dolores (coord.)
  2. Makhortykh, Mykola (coord.)
  3. Segado-Boj, Francisco (coord.)

ISSN: 1134-3478

Any de publicació: 2022

Títol de l'exemplar: Discursos de odio en comunicación: Investigaciones y propuestas

Número: 71

Pàgines: 63-75

Tipus: Article

DOI: 10.3916/C71-2022-05 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Altres publicacions en: Comunicar: Revista Científica de Comunicación y Educación

Resum

Los contextos de polarización social y política están generando nuevas formas de comunicar que inciden en la esfera pública digital. En estos entornos, distintos actores sociales y políticos estarían contribuyendo a extremar sus posicionamientos, utilizando «bots» para crear espacios de distanciamiento social en los que tienen cabida el discurso del odio y la «incivility», un fenómeno que preocupa a científicos y expertos. El objetivo principal de esta investigación es analizar el rol que desempeñaron estos agentes automatizados en el debate en redes sociales sobre la gestión del Gobierno de España durante la pandemia global de COVID-19. Para ello, se han aplicado técnicas de «Social Big Data Analysis»: algoritmos de «machine learning» para conocer el posicionamiento de los usuarios; algoritmos de detección de «bots»; técnicas de «topic modeling» para conocer los temas del debate en la red, y análisis de sentimiento. Se ha utilizado una base de datos compuesta por mensajes de Twitter publicados durante el confinamiento iniciado a raíz del estado de alarma español. La principal conclusión es que los «bots» podrían haber servido para diseñar una campaña de propaganda política iniciada por actores tradicionales con el objetivo de aumentar la crispación en un ambiente de emergencia social. Se sostiene que, aunque dichos agentes no son los únicos actores que aumentan la polarización, sí coadyuvan a extremar el debate sobre determinados temas clave, incrementando la negatividad.

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Referències bibliogràfiques

  • Abramowitz, A.I. (2010). The disappearing center. Yale University Press. https://bit.ly/3s7UlwC
  • Adlung, S., Lünenborg, M., & Raetzsch, C. (2021). Pitching gender in a racist tune: The affective publics of the# 120decibel campaign. Media and Communication, 9(2), 16-26. https://doi.org/10.17645/mac.v9i2.3749
  • Al-Rawi, A., & Shukla, V. (2020). Bots as active news promoters: A digital analysis of COVID-19 tweets. Information, 11(10), 461. https://doi.org/10.3390/info11100461
  • Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-36. https://doi.org/10.1257/jep.31.2.211
  • Barberá, P., Jost, J.T., Nagler, J., Tucker, J.A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological science, 26(10), 1531-1542. https://doi.org/10.1177/0956797615594620
  • Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2013). Design and analysis of a social botnet. Computer Networks, 57(2), 556-578. https://doi.org/10.1016/j.comnet.2012.06.006
  • Boxell, L., Gentzkow, M., & Shapiro, & J.M. (2017). Is the internet causing political polarization? Evidence from demographics. National Bureau of Economic Research. https://doi.org/10.3386/w23258
  • Bradshaw, S., & Howard, P.N. (2019). The global disinformation order: 2019 global inventory of organised social media manipulation. Oxford Internet Institute. https://acortar.link/puyazU
  • Calvo, E., & Aruguete, N. (2020). Fake News, trolls y otros encantos. Cómo funcionan (para bien y para mal) las redes sociales. Siglo XXI. https://doi.org/10.22201/fcpys.24484911e.2020.29.76061
  • Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of Communication, 64(2), 317-332, https://doi.org/10.1111/jcom.12084
  • Pita-Fernández , S. (1996). Determinación del tamaño muestral. Cad Aten Primaria, 3(138-14), 1-6. https://bit.ly/3DYcijz
  • Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96-104. https://doi.org/10.1145/2818717
  • Fiorina, M.P., & Abrams, S.J. (2008). Political polarization in the American public. Annual Review of Political Science, 11, 563-588. https://doi.org/10.1146/annurev.polisci.11.053106.153836
  • Grün, B., & Hornik, K. (2011). Topicmodels: An R package for fitting topic models. Journal of Statistical Software, 40(13), 1-30. https://doi.org/10.18637/jss.v040.i13
  • Guevara, J.A., Gómez, D., Robles, J M., & Montero, J. (2020). Measuring polarization: A fuzzy set theoretical approach. In M.J. Lesot, S. Vieira, M.z. Reformat, J.O. Carvalho, A. Wilbik, B. Bouchon-Meunier & R.R. Yager, (Eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 510-522). Springer. https://doi.org/10.1007/978-3-030-50143-3_40
  • Hansen, L.K., Arvidsson, A., Nielsen, F.A., Colleoni, E., & Etter, M. (2011). Good friends, bad news-affect and virality in twitter. In J.J. Park, L.T. Yang, C. Lee (Eds.), Future information technology (pp. 34-43). Springer. https://doi.org/10.1007/978-3-642-22309-9_5
  • Howard, P.N. (2006). New media campaigns and the managed citizen. Cambridge University Press. https://doi.org/10.1080/10584600701641532
  • Howard, P.N., Woolley, S., & Calo, R. (2018). Algorithms, bots, and political communication in the U.S. 2016 election: The challenge of automated political communication for election law and administration. Journal of Information Technology & Politics, 15(2), 81-93. https://doi.org/10.1080/19331681.2018.1448735
  • Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N., & Westwood, S.J. (2019). The origins and consequences of affective polarization in the United States. Annual Review of Political Science, 22(1), 129-146, https://doi.org/10.1146/annurev-polisci-051117-073034
  • 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
  • Keller, F.B., Schoch, D., Stier, S., & Yang, J.H. (2019). Political astroturfing on Twitter: How to coordinate a disinformation campaign. Political Communication, 37(2), 256-280. https://doi.org/10.1080/10584609.2019.1661888
  • Keller, T.R., & Klinger, U. (2019). Social bots in election campaigns: Theoretical, empirical, and methodological implications. Political Communication, 36(1), 171-189. https://doi.org/10.1080/10584609.2018.1526238
  • Kovic, M., Rauchfleisch, A., Sele, M., & Caspar, C. (2018). Digital astroturfing in politics: Definition, typology, and countermeasures. Studies in Communication Sciences, 18(1), 69-85. https://doi.org/10.24434/j.scoms.2018.01.005
  • Lelkes, Y. (2016). Mass polarization: Manifestations and measurements. Public Opinion Quarterly, 80(1), 392-410. https://doi.org/10.1093/poq/nfw005
  • Luengo, O., García-Marín, J., & De-Blasio, E. (2021). COVID-19 on YouTube: Debates and polarisation in the digital sphere. [COVID-19 en YouTube: Debates y polarización en la esfera digital]. Comunicar, 69, 9-19. https://doi.org/10.3916/C69-2021-01
  • Martini, F., Samula, P., Keller, T.R., & Klinger, U. (2021). Bot, or not? Comparing three methods for detecting social bots in five political discourses. Big Data & Society, 8(2). https://doi.org/10.1177/20539517211033566
  • Moffitt, J.D., King, C., & Carley, K.M. (2021). Hunting conspiracy theories during the COVID-19 pandemic. Social Media+Society, 7(3). https://doi.org/10.1177/20563051211043212
  • Morgan, S. (2018). Fake news, disinformation, manipulation and online tactics to undermine democracy. Journal of Cyber Policy, 3(1), 39-43. https://doi.org/10.1080/23738871.2018.1462395
  • Mueller, S.D., & Saeltzer, M. (2020). Twitter made me do it! Twitter's tonal platform incentive and its effect on online campaigning. Information, Communication & Society, 1-26. https://doi.org/10.1080/1369118X.2020.1850841
  • Neyazi, T.A. (2019). Digital propaganda, political bots and polarized politics in India. Asian Journal of Communication, 30(1), 39-57. https://doi.org/10.1080/01292986.2019.1699938
  • Papacharissi, Z. (2004). Democracy online: Civility, politeness, and the democratic potential of online political discussion groups. New media & society, 6(2), 259-283. https://doi.org/10.1177/1461444804041444
  • Pastor-Galindo, J., Nespoli, P., Gómez-Mármol, F., & Mártinez-Pérez, G. (2020). Spotting political social bots in Twitter: A use case of the 2019 Spanish general election. IEEE Transactions on Network and Service Management, 8, 10282-10304. https://doi.org/10.1109/access.2020.2965257
  • Persily, N. (2017). The 2016 U.S. election: Can democracy survive the internet? Journal of Democracy, 28(2), 63-76. https://doi.org/10.1353/jod.2017.0025
  • Price, K.R., Priisalu, J., & Nomin, S. (2019). Analysis of the impact of poisoned data within twitter classification models. IFAC-PapersOnLine, 52(19), 175-180. https://doi.org/10.1016/j.ifacol.2019.12.170
  • Prior, M. (2013). Media and political polarization. Annual Review of Political Science, 16, 101-127. https://doi.org/10.1146/annurev-polisci-100711-135242
  • Rowe, I. (2015). Civility 2.0: A comparative analysis of incivility in online political discussion. Information, Communication & Society, 18(2), 121-138. https://doi.org/10.1080/1369118X.2014.940365
  • Santana, L.E., & Huerta-Cánepa, G. (2019). ¿Son bots? Automatización en redes sociales durante las elecciones presidenciales de Chile 2017. Cuadernos.info, 44, 61-77. https://doi.org/10.7764/cdi.44.1629
  • Sartori, G. (2005). Parties and party systems: A framework for analysis. ECPR press.
  • Serrano-Contreras, I.J., García-Marín, J., & Luengo, O.G. (2020). Measuring online political dialogue: Does polarization trigger more deliberation? Media and Communication, 8(4), 63-72. https://doi.org/10.17645/mac.v8i4.3149
  • Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F., & Yang, K.C. (2018). The spread of low-credibility content by social bots. Nature Communication, 9(1), 1-10. https://doi.org/10.1038/s41467-018-06930-7
  • Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2020). Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big data, 8(3), 171-188. http://doi.org/10.1089/big.2020.0062
  • Sobieraj, S., & Berry, J.M. (2011). From incivility to outrage: Political discourse in blogs, talk radio, and cable news. Political Communication, 28(1), 19-41. https://doi.org/10.1080/10584609.2010.542360
  • Stella, M., Ferrara, E., & De-Domenico, M. (2018). Bots increase exposure to negative and inflammatory content in online social systems. In J. Kleinberg (Ed.), Proceedings of the National Academy of Sciences, 115(49), 12435-12440. https://doi.org/10.1073/pnas.1803470115
  • Sunstein, C.R. (2001). Designing democracy: What constitutions do? Oxford University Press.
  • Sunstein, C.R. (2018). #Republic. Princeton university press.
  • Taber, C.S., & Lodge, M. (2006). Motivated skepticism in the evaluation of political beliefs. American journal of political science, 50(3), 755-769. https://doi.org/10.1111/j.1540-5907.2006.00214.x
  • Uyheng, J., & Carley, K.M. (2020). Bots and online hate during the COVID-19 pandemic: Case studies in the United States and the Philippines. J Comput Soc Sc, 3, 445-468. https://doi.org/10.1007/s42001-020-00087-4
  • Walker, E.T. (2014). Grassroots for hire: Public affairs consultants in American democracy. Cambridge University Press. https://doi.org/10.1017/CBO9781139108829
  • Yan, H.Y., Yang, K., Menczer, F., & Shanahan, J. (2020). Asymmetrical perceptions of partisan political bots. New Media & Society, 23(10), 3016-3037. https://doi.org/10.1177/1461444820942744
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