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

When negativity is the fuel. Bots and Political Polarization in the COVID-19 debate

 José-Manuel Robles, Madrid (España)

 Juan-Antonio Guevara, Madrid (España)

 Belén Casas-Mas, Madrid (España)

 Daniel Gómez, Madrid (España)

https://doi.org/10.3916/C71-2022-05

Palabras clave

COVID-19, bots políticos, polarización política, propaganda digital, opinión pública, análisis de redes sociales

Resumen

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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Fundref

Grupo de investigación Data Science and Soft Computing for Social Analytics and Decision Aid. Esta investigación ha sido apoyada por los proyectos de investigación nacional financiados por el Gobierno de España, con referencia I+D+i, PID2019-106254RB-100 financiación: MINECO (Duración: 2020-2024) y PGC2018-096509B-I00.

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Ficha técnica

Recibido: 04-10-2021

Revisado: 19-10-2021

Aceptado: 03-12-2021

OnlineFirst: 01-02-2022

Fecha publicación: 01-04-2022

Tiempo de revisión del artículo : 15 (en días) | Media de tiempo de revisión de los manuscritos del número 71: 45 (en días)

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