Disentangling positive and negative partisanship in social media interactions using a coevolving latent space network with attractors model
Abstract
We develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction between actors, and attractors are added in the latent level to capture the notion of attractive and repulsive forces. We apply the CLSNA models to understand the dynamics of partisan polarization in US politics on social media, where we expect Republicans and Democrats to increasingly interact with their own party and disengage with the opposing party. Using longitudinal social networks from the social media platforms Twitter and Reddit, we quantify the relative contributions of positive (attractive) and negative (repulsive) forces among political elites and the public, respectively.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 25, 2023
- Source ID
- 10.1093/jrsssa/qnad008
Entities
People
- Cantay Caliskan
- Dino P Christenson
- Dylan Walker
- Eric D. Kolaczyk
- Konstantinos Spiliopoulos
- Xiaojing Zhu
Organizations
- Army Research Office
- Boston University
- Chapman University
- McGill University
- National Science Foundation
- University of Rochester
- Washington University in St. Louis