# Quantifying Information Distribution in Social Networks: The Structural Entropy Index of Community (SEIC) for Twitter Communication Analysis

## TL;DR

This paper introduces a new method to analyze Twitter networks using graph theory and information theory, revealing how information spreads in political discussions.

## Contribution

The novel Structural Entropy Index of a Community (SEIC) quantifies communication decentralization within social network communities.

## Key findings

- Larger Twitter communities tend to have decentralized communication (SEIC > 0.8).
- Smaller groups are often dominated by a few influential users.
- The SEIC metric provides insights into disinformation resilience and communication strategies.

## Abstract

This paper presents an integrated approach to social network analysis that combines graph theory, social network analysis (SNA), and Shannon’s information theory, applied to a real-world Twitter network built around the political hashtag Zandberg. Unlike studies based on synthetic data, our analysis leverages empirical content from a live political discourse. We employ classical centrality measures (degree, betweenness, closeness), local clustering coefficients, and community detection using the Louvain algorithm. A key theoretical contribution is the introduction of a novel metric: the Structural Entropy Index of a Community (SEIC), which quantifies internal decentralization of communication independently of community size. The analysis reveals significant variation in community structures and entropy levels. Larger communities tend to be decentralized (SEIC > 0.8), while smaller groups are often dominated by single influential nodes. These findings have practical implications for influencer identification, disinformation resilience assessment, and communication strategy optimization. The proposed methodological framework provides a robust tool for studying the structural and informational dynamics of real-world social networks in digital environments.