Science Visualization and Discursive Knowledge

TL;DR

This paper explores the relationship between network data analysis and textual analysis, highlighting their different perspectives and dynamics, and discusses how their integration enhances understanding of complex information systems.

Contribution

It introduces a framework linking social network analysis and latent semantic analysis, emphasizing their coupled dynamics and evolutionary development.

Findings

Different dynamics in network and textual analysis

Historical and system dynamics are interconnected

Evolutionary differentiation enriches information processing

Abstract

Positional and relational perspectives on network data have led to two different research traditions in textual analysis and social network analysis, respectively. Latent Semantic Analysis (LSA) focuses on the latent dimensions in textual data; social network analysis (SNA) on the observable networks. The two coupled topographies of information-processing in the network space and meaning-processing in the vector space operate with different (nonlinear) dynamics. The historical dynamics of information processing in observable networks organizes the system into instantiations; the systems dynamics, however, can be considered as self-organizing in terms of fluxes of communication along the various dimensions that operate with different codes. The development over time adds evolutionary differentiation to the historical integration; a richer structure can process more complexity.