The Polarization Loop: How Emotions Drive Propagation of Disinformation in Online Media—The Case of Conspiracy Theories and Extreme Right Movements in Southern Europe

Abstract

first_pagesettingsOrder Article Reprints Open AccessArticle The Polarization Loop: How Emotions Drive Propagation of Disinformation in Online Media—The Case of Conspiracy Theories and Extreme Right Movements in Southern Europe by Erik Bran Marino 1ORCID,Jesus M. Benitez-Baleato 2,*ORCID andAna Sofia Ribeiro 1ORCID 1 CIDEHUS—Centro Interdisciplinar de História, Culturas e Sociedades, Universidade de Évora, Palácio do Vimioso, Largo do Marquês de Marialva, n.º 8, 7000-809 Évora, Portugal 2 Political Analysis Research Group (Grupo de Investigacións Políticas), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain * Author to whom correspondence should be addressed. Soc. Sci. 2024, 13(11), 603; https://doi.org/10.3390/socsci13110603 Submission received: 19 August 2024 / Revised: 18 October 2024 / Accepted: 21 October 2024 / Published: 5 November 2024 (This article belongs to the Special Issue Disinformation in the Public Media in the Internet Society) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract This paper examines the influence of emotions on political polarization, looking at online propagation of conspiracy thinking by extreme right movements in Southern Europe. Integrating insights from psychology, political science, media studies, and system theory, we propose the ‘polarization loop’, a causal mechanism explaining the cyclical relationship between extreme messages, emotional engagement, media amplification, and societal polarization. We illustrate the utility of the polarization loop observing the use of the Great Replacement Theory by extreme right movements in Italy, Portugal, and Spain. We suggest possible options to mitigate the negative effects of online polarization in democracy, including public oversight of algorithmic decission-making, involving social science and humanities in algorithmic design, and strengthening resilience of citizenship to prevent emotional overflow. We encourage interdisciplinary research where historical analysis can guide computational methods such as Natural Language Processing (NLP), using Large Language Models fine-tunned consistently with political science research. Provided the intimate nature of emotions, the focus of connected research should remain on structural patterns rather than individual behavior, making it explicit that results derived from this research cannot be applied as the base for decisions, automated or not, that may affect individuals.