Understanding Twitter Dynamics With R and Gephi: Text Analysis and Centrality

, Software Pundits
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Toptal

This article expands upon and deepens the analysis presented in the first installment of our social network analysis series. We use the same Twitter dataset and interaction network constructed in the first article. However, this time, the idea is to infer the principal actors better, identify their discussion topics, and understand how these topics spread.

Social Network Centrality

To achieve our goals, first we need to introduce the concept of centrality. In network science, centrality refers to nodes that have a strong influence on the network. Influence is an ambiguous concept; it can be understood in many ways. Is a node with many edges more influential than a node with fewer but more “important” edges? What constitutes an important edge on a social network?

To address these ambiguities, network scientists have developed many measures of centrality. Here, we discuss four commonly used measures, though many more are available.

Degree

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