Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election

Area of Science:

• Social Network Analysis
• Computational Social Science
• Political Communication

Background:

• The 2020 US presidential election saw widespread misinformation on social media.
• Large social network datasets are challenging to interpret due to complexity.
• Visualizing dense networks obscures important structural features.

Purpose of the Study:

• To develop a method for visualizing complex social network data.
• To analyze the structure of social media discourse surrounding the 2020 US election.
• To identify and characterize key network structures within political conversations.

Main Methods:

• Applied coengagement transformations to convert network data into visual representations.
• Created parameterized network visualizations highlighting shared audiences.
• Conducted a case study of the 2020 United States presidential election on Twitter.

Main Results:

• Identified and characterized structures like bridging accounts, satellite audiences, and followback communities.
• Demonstrated the utility of coengagement transformations for understanding complex networks.
• Provided empirical analysis of social media discourse during a major political event.

Conclusions:

• Coengagement transformations offer a tractable method for analyzing large social networks.
• Network structures identified provide insights into political discourse dynamics.
• Open-source code is released to facilitate further research in social network analysis.