Polarization on Social Media: Micro-Level Evidence and Macro-Level Implications

We formally introduce and empirically test alternative micro-foundations of social influence in the context of communication on social media. To this end, we first propose a general theoretical framework allowing us to represent by different combinations of model parameters of influence-response functions whether and to what extent exposure to online content leads to assimilative or repulsive (distancing) influence on a user's opinion. We show by means of an agent-based model of opinion dynamics on social media that these influence-response functions indeed generate competing predictions about whether personalization (defined as the extent to which users are shielded from foreign opinions) increases or decreases polarization. Next, we conducted an online experiment with respondents recruited on Facebook to estimate model parameters empirically, using Bayesian models for censored data. In the experiment, participants´ opinions were measured before and after exposure to arguments reflecting different ideological positions and moral foundations in a within-subject design. Our findings support that exposure to foreign opinions leads to assimilation towards those opinions, moderated by the extent of (perceived) ideological similarity to the source of influence. We also find weak evidence in support of repulsion (distancing), but only for very large disagreement. Feeding estimated parameter values back into the agent-based model suggests that reducing personalization would not increase, but instead reduce the level of polarization generated by the opinion dynamics of the model. We conclude that the naive interpolation of micro-processes to macro-predictions can be misleading if models are not sufficiently empirically calibrated.