Community Detection in Political Twitter Networks using Nonnegative Matrix Factorization Methods

TL;DR

This paper introduces new nonnegative matrix factorization methods for community detection in political Twitter networks, leveraging endorsement filtering, user content, and word similarity to improve clustering accuracy.

Contribution

It develops three NMF frameworks that integrate connectivity and content data, enhancing community detection in political Twitter networks.

Findings

User content and endorsement filtering are complementary for community detection.

Word usage is the strongest indicator of political orientation.

Incorporating user-word matrices improves cluster detection accuracy.

Abstract

Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate the contributions of different types of user connectivity and content information in community detection. We show that user content and endorsement filtered connectivity information are complementary to each other in clustering politically motivated users into pure political communities. Word usage is the strongest indicator of users' political orientation among all content categories. Incorporating user-word matrix and word similarity regularizer provides the missing link in connectivity only methods which suffer from detection of artificially large number of clusters for Twitter networks.