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

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.