ItemArticle

Thermodynamic Modeling of Hashtag Dynamics for Social Media Clustering: A Maxwell-Boltzmann Approach

Title
Thermodynamic Modeling of Hashtag Dynamics for Social Media Clustering: A Maxwell-Boltzmann Approach
Creator
Batri, Krishnan; Thinakaran, Rajermani; Jayabalan, Bhuvana; Karthikeyan, L.; Lakshmi, S.; Sowrirajan, R.; Murugan, Sivaram
Description
Social media hashtags function as critical organizational markers in digital discourse, yet traditional weighting methods fail to capture their dynamic significance across temporal and contextual dimensions. This paper presents a novel thermodynamic framework that conceptualizes social network activity as system 'temperature', applying statistical mechanics principles to model hashtag importance as process innovation. We establish mathematical foundations based on the Maxwell-Boltzmann distribution, providing an information-theoretic justification for dynamic hashtag weighting. Our approach incorporates activation thresholds and power-law scaling behaviors through a temperature-dependent function, with Simple Moving Average techniques implemented to stabilize temperature estimation, mathematically reducing variance by a factor of 1/N. Empirical evaluation using Twitter discourse from the US Presidential Election demonstrates unprecedented improvements in clustering performance: Silhouette Scores increased from 0.0126 to 0.9070 for Trump-related content and from 0.0105 to 0.8220 for Biden-related content, while Calinski-Harabasz Scores improved from 65.51 to nearly 98 million. These findings establish a rigorous mathematical bridge between thermodynamic systems and social media behavior, contributing to computational social science by providing a theoretical framework that significantly enhances discourse community detection in politically polarized environments. The approach enables more accurate identification of topic clusters, revealing distinct discourse patterns that conventional methods fail to capture. 2025 The Authors.
Source
IEEE Access;Volume;13;pp.145556-145571
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
computational thermodynamics; dynamic weighting; hashtag dynamics; Maxwell-Boltzmann distribution; political discourse analysis; Social media analytics; social network clustering; statistical mechanics
Coverage
Batri K., Sharda University, Department of Computer Science and Engineering, Uttar Pradesh, Greater Noida, 201310, India; Thinakaran R., Inti International University, Faculty of Data Science and Information Technology, Negeri Sembilan, Nilai, 71800, Malaysia; Jayabalan B., Christ University, School of Sciences, Department of Computer Science, Bengaluru, 560029, India; Karthikeyan L., Saveetha Institute of Medical and Technical Sciences, Saveetha School of Engineering, Department of Computer Science and Engineering, Tamil Nadu, Thandalam, 602105, India; Lakshmi S., Sharda University, Department of Computer Science and Engineering, Uttar Pradesh, Greater Noida, 201310, India; Sowrirajan R., Dr. N.G.P. Arts and Science College, Department of Mathematics, Tamil Nadu, Coimbatore, 641048, India; Murugan S., Sivas University of Science and Technology, Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Sivas, 58000, Turkey
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 21693536;
Format
online
Language
English
Type
Article

Citation

Batri, Krishnan; Thinakaran, Rajermani; Jayabalan, Bhuvana; Karthikeyan, L.; Lakshmi, S.; Sowrirajan, R.; Murugan, Sivaram, “Thermodynamic Modeling of Hashtag Dynamics for Social Media Clustering: A Maxwell-Boltzmann Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22952.

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