Community Detection in Political Twitter Networks using Nonnegative Matrix Factorization Methods
This addresses the problem of detecting political communities in social networks for researchers and analysts, but it is incremental as it builds on existing methods with specific adaptations.
The paper tackled community detection in political Twitter networks by developing endorsement filtered connectivity and three Nonnegative Matrix Factorization frameworks, showing that combining user content and connectivity information clusters users into pure political communities with word usage as the strongest indicator of political orientation.
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.