Multidimensional political polarization in online social networks

TL;DR

This study analyzes the structure and dynamics of political polarization on French Twitter, revealing how community cohesion, opinion extremity, and interaction patterns contribute to online political divides.

Contribution

It introduces a multidimensional opinion space combining network embedding and survey data, providing new insights into polarization mechanisms in digital social networks.

Findings

Polarized communities are structurally cohesive and share common political attitudes.

Extreme political groups interact less with others, indicating social segregation.

The social influence model explains polarization dynamics, especially among centrists.

Abstract

Political polarization in online social platforms is a rapidly growing phenomenon worldwide. Despite their relevance to modern-day politics, the structure and dynamics of polarized states in digital spaces are still poorly understood. We analyze the community structure of a two-layer, interconnected network of French Twitter users, where one layer contains members of Parliament and the other one regular users. We obtain an optimal representation of the network in a four-dimensional political opinion space by combining network embedding methods and political survey data. We find structurally cohesive groups sharing common political attitudes and relate them to the political party landscape in France. The distribution of opinions of professional politicians is narrower than that of regular users, indicating the presence of more extreme attitudes in the general population. We find that politically extreme communities interact less with other groups as compared to more centrist groups. We apply an empirically tested social influence model to the two-layer network to pinpoint interaction mechanisms that can describe the political polarization seen in data, particularly for centrist groups. Our results shed light on the social behaviors that drive digital platforms towards polarization, and uncover an informative multidimensional space to assess political attitudes online.