Social media feedback and extreme opinion expression

On popular social media platforms such as Twitter, Facebook, Instagram, or Tiktok, the quantitative feedback received by content producers is asymmetric: counts of positive reactions such as ‘likes,’ or ‘retweets,’ are easily observed but similar counts of negative reactions are not directly available. We study how this design feature of social media platforms affects the expression of extreme opinions. Using simulations of a learning model, we compare two feedback environments that differ in terms of the availability of negative reaction counts. We find that expressed opinions are generally more extreme when negative reaction counts are not available than when they are. We rely on analyses of Twitter data and several online experiments to provide empirical support for key model assumptions and test model predictions. Our findings suggest that a simple design change might limit, under certain conditions, the expression of extreme opinions on social media

Opinion homogenization and polarization

We describe three sampling models that aim to cast light on how some design features of social media platforms systematically affect judgments of their users. We specify the micro-mechanisms of belief formation and interactions and explore their macro implications such as opinion polarization. Each model focuses on a specific aspect of platform-mediated social interactions: how popularity creates additional exposure to contrarian arguments; how differences in popularity make an agent more likely to hear particularly persuasive arguments in support of popular options; and how opinions in favor of popular options are reinforced through social feedback. We show that these mechanisms lead to self-reinforcing dynamics that can result in local opinion homogenization and between-group polarization. Unlike nonsampling-based approaches, our focus does not lie in peculiarities of information processing such as motivated cognition but instead emphasizes how structural features of the learning environment contribute to opinion homogenization and polarization