Disentangling positive and negative partisanship in social media interactions using a coevolving latent space network with attractors model
Xiaojing Zhu, Cantay Caliskan, Dino P. Christenson, Konstantinos, Spiliopoulos, Dylan Walker, Eric D. Kolaczyk

TL;DR
This paper introduces a novel coevolving latent space network model with attractors to analyze social media interactions, revealing how positive and negative partisan forces influence polarization among political actors over time.
Contribution
The paper develops the CLSNA model to disentangle positive and negative partisan forces in social media networks, providing new insights into polarization dynamics.
Findings
Partisan polarization exists among social media users and elites.
Positive partisanship drives interactions across the entire study period.
Negative partisanship has increased among Republican elites since 2016.
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 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 investigate the relative contributions of positive (attractive) and negative (repulsive) forces among political elites and the public, respectively. Our goals are to disentangle the positive and negative forces within…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Social Media and Politics · Complex Network Analysis Techniques
