Leadership and Engagement Dynamics in Legislative Twitter Networks: Statistical Analysis and Modeling

TL;DR

This study analyzes Twitter interactions among U.S. Congress members to understand how systemic and individual factors influence legislative network formation and leadership visibility.

Contribution

It applies social network statistical methods, ERGM, and SBM to reveal how systemic and personal traits shape online legislative connections.

Findings

Twitter networks reinforce dominant political leaders.

Leadership roles vary in form within the network.

Network properties and institutional traits influence connections.

Abstract

In this manuscript, we analyze the interaction network on Twitter among members of the 117th U.S. Congress to assess the visibility of political leaders and explore how systemic properties and node attributes influence the formation of legislative connections. We employ descriptive social network statistical methods, the exponential random graph model (ERGM), and the stochastic block model (SBM) to evaluate the relative impact of network systemic properties, as well as institutional and personal traits, on the generation of online relationships among legislators. Our findings reveal that legislative networks on social media platforms like Twitter tend to reinforce the leadership of dominant political actors rather than diminishing their influence. However, we identify that these leadership roles can manifest in various forms. Additionally, we highlight that online connections within legislative networks are influenced by both the systemic properties of the network and institutional characteristics.