  A network is a composition of many communities, i.e., sets of nodes and edges
with stronger relationships, with distinct and overlapping properties.
Community detection is crucial for various reasons, such as serving as a
functional unit of a network that captures local interactions among nodes.
Communities come in various forms and types, ranging from biologically to
technology-induced ones. As technology-induced communities, social media
networks such as Twitter and Facebook connect a myriad of diverse users,
leading to a highly connected and dynamic ecosystem. Although many algorithms
have been proposed for detecting socially cohesive communities on Twitter,
mining and related tasks remain challenging. This study presents a novel
detection method based on a scalable framework to identify related communities
in a network. We propose a multilevel clustering technique (MCT) that leverages
structural and textual information to identify local communities termed
microcosms. Experimental evaluation on benchmark models and datasets
demonstrate the efficacy of the approach. This study contributes a new
dimension for the detection of cohesive communities in social networks. The
approach offers a better understanding and clarity toward describing how
low-level communities evolve and behave on Twitter. From an application point
of view, identifying such communities can better inform recommendation, among
other benefits.
