Socio-Spatial Pareto Frontiers of Twitter Networks

TL;DR

This paper reviews a community detection method for multilayer networks and demonstrates its application to Twitter data related to the NFL, providing insights into socio-spatial network structures.

Contribution

It introduces a community detection approach for multilayer networks and applies it to analyze Twitter data, offering a visualization tool for socio-spatial network analysis.

Findings

Community detection applied to NFL Twitter data reveals distinct socio-spatial communities.

The multilayer network approach captures multiple relation types in social media.

Visualization aids in understanding complex community structures.

Abstract

Social media provides a rich source of networked data. This data is represented by a set of nodes and a set of relations (edges). It is often possible to obtain or infer multiple types of relations from the same set of nodes, such as observed friend connections, inferred links via semantic comparison, or relations based off of geographic proximity. These edge sets can be represented by one multi-layer network. In this paper we review a method to perform community detection of multilayer networks, and illustrate its use as a visualization tool for analyzing different community partitions. The algorithm is illustrated on a dataset from Twitter, specifically regarding the National Football League (NFL).