Discovering Multi-Scale Community Structures from the Interpersonal Communication Network on Twitter

Abstract

Despite the controversies of privacy and ethics, spatially-embedded communication data from widespread and emerging online social networks provide an unprecedented opportunity to study human interactions at the global scale. Detecting communities of individuals who live close by and have strong communication among each other is critical for a variety of application areas such as managing disaster response, controlling disease spread, and developing sustainable urban spaces and infrastructure. The ease of long-distance travel and communication have generated a highly complex network of human interactions, in which long-distance and short-distance ties coexist in multiple scales. Also, there is a hierarchical spatial organization in human interaction networks which reflect historic and socio-political borders. Patterns of human connectivity cross these historic and socio-political borders at multiple geographic scales. Therefore, a comprehensive understanding of human interactions necessitates analysis methods to take into account the complexity introduced by the multi-scale nature of human connectivity. This paper employs a spatially-constrained hierarchical regionalization algorithm to reveal multi-scale community structures in the interpersonal communication network on Twitter. The interpersonal communication network was constructed using a year of reciprocal and geo-located mention tweets in the U.S. between August 2015 and 2016. The results strikingly showed nested borders of cohesive regions at multiple scales, which are inherent to human communication patterns in the regional hierarchy of the U.S. Unsurprisingly, people communicated with others that live nearby, and multi-scale regions overlap with administrative boundaries of the states, cultural and dialectal regions, and topographical features. Furthermore, visualization of interregional communication patterns revealed a variety of spatial connectivity patterns such as poly-centricity, hierarchies, and spanning trees. Discovery of such patterns is essential for understanding of the complex social system that is influenced by long-distance ties.