Suchergebnisse

Several authors have raised in recent years that the new scenario generated by digital communication causes citizens to have a role with greater prominence in the public debate. However,apart from the multiple political and communicative opportunities arising from the irruption of digital social networks, we find some risks and limitations relevant to political communication such as lack of civility (Incivility). The objective of this research is to analyze, through the study of the case of "La Manada" (of great social and political repercussion in Spain) and making use of Big data techniques, the possible polarization of public debate in the social network Twitter and the existence of this type of negative practices, especially when they affect women. We find a scenario of extreme polarization in which both parties tend to use the subject of debate as a pretext to reinforce the positions that separate the left and the right in Spain. ; Diversos autores han planteado en los últimos años que el nuevo escenario que genera la comunicación digital provoca que los ciudadanos tengan un papel con mayor protagonismo en el debate público. Sin embargo, al margen de las múltiples oportunidades políticas y comunicativas surgidas de tras la irrupción de las redes sociales digitales, encontramos algunos riesgos y limitaciones relevantes para la comunicación política como la falta de civismo (Incivility). El objetivo de esta investigación es analizar, a través del estudio del caso de "La manada" (de gran repercusión social y política en España) y haciendo uso de técnicas de Big data, la posible polarización de debate público en la red social Twitter y la existencia de este tipo de prácticas negativas, en especial, cuando afectan a las mujeres. Encontramos un escenario de una extrema polarización en el que ambas partes tienden a usar el tema de debate como pretexto para reforzar las posiciones que separan a la izquierda y la derecha en España.

The contexts of social and political polarization are generating new forms of communication that affect the digital public sphere. In these environments, different social and political actors contribute to extreme their positions, using bots to create spaces for social distancing where hate speech and incivility have a place, a phenomenon that worries scientists and experts. The main objective of this research is to analyze the role that these automated agents played in the debate on social networks about the Spanish Government's management of the global COVID-19 pandemic. For this, "Social Big Data Analysis" techniques were applied: "machine learning algorithms to know the positioning of users; bot detection algorithms; "topic modeling" techniques to learn about the topics of the debate on the web, and sentiment analysis. We used a database comprised of Twitter messages published during the confinement, as a result of the Spanish state of alarm. The main conclusion is that the bots could have served to design a political propaganda campaign initiated by traditional actors with the aim of increasing tension in an environment of social emergency. It is argued that, although these agents are not the only actors that increase polarization, they do contribute to deepening the debate on certain key issues, increasing negativity.

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 ; The contexts of social and political polarization are generating new forms of communication that affect the digital public sphere. In these environments, different social and political actors contribute to extreme their positions, using bots to create spaces for social distancing where hate speech and incivility have a place, a phenomenon that worries scientists and experts. The main objective of this research is to analyze the role that these automated agents played in the debate on social networks about the Spanish Government's management of the global COVID-19 pandemic. For this, "Social Big Data Analysis" techniques were applied: machine learning algorithms to know the positioning of users; bot detection algorithms; "topic modeling" techniques to learn about the topics of the debate on the web, and sentiment analysis. We used a database comprised of Twitter messages published during the confinement, as a result of the Spanish state of alarm. The main conclusion is that the bots could have served to design a political propaganda campaign initiated by traditional actors with the aim of increasing tension in an environment of social emergency. It is argued that, although these agents are not the only actors that increase polarization, they do contribute to deepening the debate on certain key issues, increasing negativity