Classifying Twitter Topic-Networks Using Social Network Analysis

TLDR

On Twitter, user interactions create complex social network structures that reflect content sharing and information flow patterns. The study aims to classify Twitter conversations into topical network structures using overall network-level patterns of information flow. The authors employ a three-step classification model that uses density, modularity, centralization, and isolated-user fraction metrics to identify and interpret six distinct network structures. The model identifies six distinct information‑flow structures—divided, unified, fragmented, clustered, in‑hub, and out‑hub—and classifies 60 Twitter topical networks accordingly, revealing topic‑specific flow patterns.

Abstract

As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network structures by segmenting 60 Twitter topical social media network datasets into these six distinct patterns of collective connections, illustrating how different topics of conversations exhibit different patterns of information flow. We discuss conceptual and practical implications for each structure.