Untangling the Furball: A Practice Mapping Approach to the Analysis of Multimodal Interactions in Social Networks

TL;DR

This paper presents practice mapping, a novel analytical framework using vector embeddings to analyze multimodal social media interactions, overcoming limitations of traditional network analysis and enabling cross-platform studies.

Contribution

It introduces practice mapping as a new methodological approach that integrates multiple interaction modes and account-level data into social network analysis.

Findings

Practice mapping effectively captures social media interaction patterns.

It enhances analysis by incorporating textual, demographic, and cross-platform data.

The approach provides meaningful insights where traditional methods fall short.

Abstract

This article introduces the analytical approach of practice mapping, using vector embeddings of network actions and interactions to map commonalities and disjunctures in the practices of social media users, as a framework for methodological advancement beyond the limitations of conventional network analysis and visualisation. In particular, the methodological framework we outline here has the potential to incorporate multiple distinct modes of interaction into a single practice map, can be further enriched with account-level attributes such as information gleaned from textual analysis, profile information, available demographic details, and other features, and can be applied even to a cross-platform analysis of communicative patterns and practices. The article presents practice mapping as an analytical framework and outlines its key methodological considerations. Given its prominence in past social media research, we draw on examples and data from the platform formerly known as Twitter in order to enable experienced scholars to translate their approaches to a practice mapping paradigm more easily, but point out how data from other platforms may be used in equivalent ways in practice mapping studies. We illustrate the utility of the approach by applying it to a dataset where the application of conventional network analysis and visualisation approaches has produced few meaningful insights.