Anatomy of Elite and Mass Polarization in Social Networks (2406.12525v2)

Abstract: In the political arena of social platforms, opposing factions of varying sizes show asymmetrical patterns, and elites and masses within these groups have divergent motivations and influence,challenging simplistic views of polarization. Yet, existing methods for quantifying polarization reduce division to a single value, assuming uniform distribution of polarization online. While this approach can confirm the observed increase in political polarization in many societies, it overlooks complexities that could explain this phenomenon. Notably, opposing groups can have unequal impacts on polarization, and the literature shows division between elites and the masses is a critical factor to consider. We propose a method to decompose existing polarization measures in order to quantify the role of groups, determined by these distinct hierarchies, in the total polarization value. We applied this method to polarized topics in the Finnish Twittersphere surrounding the 2019 and 2023parliamentary elections. Our analysis reveals two key insights: 1) The impact of opposing groups on observed polarization is rarely balanced, and 2) while elites strongly contribute to structural polarization and consistently display greater alignment across various topics, the masses have also recently experienced a surge in issue alignment, a stronger form of polarization. Our findings suggest that the masses may not be as immune to an increasingly polarized environment as previously thought. This research provides a more nuanced understanding of polarization dynamics, offering potential insights into its underlying mechanisms and evolution

• The paper introduces a hierarchical decomposition method that distinguishes elite from mass polarization in Twitter networks.
• It employs SBM and core-periphery models to partition groups and measures polarization using the AE-index and NMI.
• Temporal analysis reveals increasing elite cohesion and its significant influence on shifting polarization dynamics during Finnish elections.

An Assessment of Polarization Hierarchies in Twitter Networks during Finnish Elections

Introduction

The phenomenon of political polarization is marked by the increasing ideological divergence and diminishing dialogue between opposing factions in a society. Traditional methods of quantifying polarization often reduce this intricate phenomenon to a single numerical value, thereby missing the complexities introduced by distinct social strata within the polarized groups. This paper by Salloum et al. offers a nuanced approach to dissecting polarization by parsing it into hierarchies within each polarized group. By differentiating the polarization impact of elites and masses, the paper enhances the granularity of polarization measurement.

Methodological Approach

The authors introduce a method to quantify hierarchical polarization, applied to Twitter networks associated with the Finnish parliamentary elections of 2019 and 2023. The Twitter environment, due to its ample data and lower response bias compared to traditional surveys, proves conducive for such an analysis.

The methodology entails:

1. Detection of Polarized Groups: The network is partitioned into two polarized clusters using the Stochastic Block Model (SBM), ensuring statistical validity through a minimum description length criterion.
2. Hierarchical Decomposition: Within each polarized cluster, further partitioning is performed to distinguish between core (elite) and periphery (mass) members using core-periphery models. This enables a layered analysis of polarization.
3. Metrics for Polarization: Two key aspects of polarization are measured: structural polarization using the Adaptive EI-index (AEI) and issue alignment using normalized mutual information (NMI) at both the elite and mass levels.

Results

Structural Polarization

The paper unveils several compelling findings:

• Unequal Contribution: Across all networks, the contributions of opposing groups to overall structural polarization were imbalanced. For instance, in the climate network of 2023, right-leaning groups exerted a greater influence despite being smaller than left-leaning groups.
• Elite Dominance: The elite consistently exhibited greater internal cohesion than the mass, leading to a higher individual impact on structural polarization. The right-leaning elite group in the 2023 immigration network significantly influenced the polarization score, indicating a more organized structure.
• Temporal Shifts: Over the four-year period, some groups experienced a shift in the hierarchical contribution. For example, in the climate network, the right-leaning elite's influence grew to surpass the left-leaning group by 2023.

Issue Alignment

The degree of issue alignment, a measure of ideological consistency across various topics, displayed distinct patterns:

• Elite Alignment: Elites showed consistently higher alignment across all topic pairs, underscoring their role in shaping coherent political discourse.
• Temporal Increase: Both elite and mass alignment intensified from 2019 to 2023, with the elite alignment nearly doubling on average.

Bayesian linear regression models quantified the dependence between elite and mass alignments, affirming a positive correlation. This suggests that as elite alignment increases, so does mass alignment, hinting at elite influence over mass opinion formation.

Implications

Theoretical Implications

The findings challenge the binary perception of polarization by exposing the intricate power dynamics within polarized networks. The unequal contributions of hierarchical groups imply that future polarization models must incorporate hierarchical nuances to accurately capture the phenomenon. Furthermore, the paper elucidates that elite cohesion is a critical driver of polarization, potentially serving as a focal point for interventions aimed at mitigating polarization.

Practical Implications

From a practical standpoint, the results indicate that countering polarization requires targeted efforts towards elite groups, whose organized structures significantly enhance polarization. Additionally, the increasing alignment among the masses suggests that public discourse is becoming increasingly binary, which policymakers and social media platforms need to address to promote a more pluralistic dialogue.

Future Directions

Future research should extend this hierarchical analysis to other social media platforms and non-political domains to generalize the findings. It would also be beneficial to explore the temporal dynamics of hierarchical structures in more detail, potentially incorporating real-time data to predict and counteract polarization patterns.

Conclusion

By delineating the hierarchical components of polarization, this paper enhances the understanding of the complex interplay between elites and masses in online polarized systems. The nuanced approach reveals that elites exercise significant influence on polarization, and their alignment with various political issues has intensified over time, a trend that warrants closer scrutiny for fostering healthier public discourse.