Classifying Twitter Topic-Networks Using Social Network Analysis:

Social Media + Society

Itai Himelboim¹ 

关键词: Twitter;    social media;    information flow;    social network analytics;    network structure;   

DOI  :  10.1177/2056305117691545

学科分类：社会科学、人文和艺术（综合）

来源: Sage Journals

【 摘 要 】

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.

【 授权许可】

CC BY-NC