When facts fail: Bias, polarisation and truth in social networks

TL;DR

This paper presents a social learning model illustrating how confirmation bias can lead to persistent polarization and cascades in social networks, with empirical validation using US county data on Internet access and beliefs about global warming.

Contribution

It introduces a simple yet powerful model capturing the effects of confirmation bias on opinion dynamics and provides empirical evidence linking Internet access to belief polarization.

Findings

Confirmation bias causes permanent polarization and cascades.

Internet access correlates with stronger beliefs about global warming.

Debunking strategies may have unintended negative effects.

Abstract

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.