Visualization and Analysis of Frames in Collections of Messages: Content Analysis and the Measurement of Meaning

TL;DR

This paper presents a step-by-step method for visualizing and analyzing the semantic structure of message collections using software tools, integrating linguistic, sociocybernetic, and communication theories to measure and communicate meaning.

Contribution

It introduces a comprehensive approach combining semantic mapping, statistical analysis, and visualization to analyze message collections within multiple theoretical frameworks.

Findings

Semantic maps reveal the structure of message collections.

Positions in vector space indicate the communicated meanings.

The approach bridges social network analysis and meaning analysis.

Abstract

A step-to-step introduction is provided on how to generate a semantic map from a collection of messages (full texts, paragraphs or statements) using freely available software and/or SPSS for the relevant statistics and the visualization. The techniques are discussed in the various theoretical contexts of (i) linguistics (e.g., Latent Semantic Analysis), (ii) sociocybernetics and social systems theory (e.g., the communication of meaning), and (iii) communication studies (e.g., framing and agenda-setting). We distinguish between the communication of information in the network space (social network analysis) and the communication of meaning in the vector space. The vector space can be considered a generated as an architecture by the network of relations in the network space; words are then not only related, but also positioned. These positions are expected rather than observed and therefore one can communicate meaning. Knowledge can be generated when these meanings can recursively be communicated and therefore also further codified.