Negative Ties Highlight Hidden Extremes in Social Media Polarization

TL;DR

This paper demonstrates that including negative ties in social media network analysis reveals hidden ideological extremes and enhances understanding of polarization, especially by identifying antagonistic users often overlooked in unsigned network models.

Contribution

It introduces a dual-method approach combining SHEEP and CA to analyze signed networks, highlighting the importance of negative ties in detecting extreme users in online polarization.

Findings

Negative ties reveal hidden ideological extremes.

Signed networks improve polarization analysis over unsigned networks.

Antagonistic behaviors are identified only when negative ties are considered.

Abstract

Human interactions in the online world comprise a combination of positive and negative exchanges. These diverse interactions can be captured using signed network representations, where edges take positive or negative weights to indicate the sentiment of the interaction between individuals. Signed networks offer valuable insights into online political polarization by capturing antagonistic interactions and ideological divides on social media platforms. This study analyzes polarization on Meneame, a Spanish social media platform that facilitates engagement with news stories through comments and voting. Using a dual-method approach, Signed Hamiltonian Eigenvector Embedding for Proximity (SHEEP) for signed networks and Correspondence Analysis (CA) for unsigned networks, we investigate how including negative ties enhances the understanding of structural polarization levels across different conversation topics on the platform. While the unsigned Meneame network effectively delineates ideological communities, only by incorporating negative ties can we identify ideologically extreme users who engage in antagonistic behaviors: without them, the most extreme users remain indistinguishable from their less confrontational ideological peers.