A minimalistic model of bias, polarization and misinformation in social networks.

Autor: Sikder O; Department of Computer Science, University College London, Gower Street, London, WC1E 6EA, UK., Smith RE; Department of Computer Science, University College London, Gower Street, London, WC1E 6EA, UK., Vivo P; Department of Mathematics, King's College London, Strand, London, WC2R 2LS, UK., Livan G; Department of Computer Science, University College London, Gower Street, London, WC1E 6EA, UK. g.livan@ucl.ac.uk.; Systemic Risk Centre, London School of Economics and Political Sciences, Houghton Street, London, WC2A 2AE, UK. g.livan@ucl.ac.uk.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2020 Mar 26; Vol. 10 (1), pp. 5493. Date of Electronic Publication: 2020 Mar 26.
DOI: 10.1038/s41598-020-62085-w
Abstrakt: Online social networks provide users with unprecedented opportunities to engage with diverse opinions. At the same time, they enable confirmation bias on large scales by empowering individuals to self-select narratives they want to be exposed to. A precise understanding of such tradeoffs is still largely missing. We introduce a social learning model where most participants in a network update their beliefs unbiasedly based on new information, while a minority of participants reject information that is incongruent with their preexisting beliefs. This simple mechanism generates permanent opinion polarization and cascade dynamics, and accounts for the aforementioned tradeoff between confirmation bias and social connectivity through analytic results. We investigate the model's predictions empirically using US county-level data on the impact of Internet access on the formation of beliefs about global warming. We conclude by discussing policy implications of our model, highlighting the downsides of debunking and suggesting alternative strategies to contrast misinformation.
Databáze: MEDLINE
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