Community Detection In Social Media By Leveraging Interactions And Intensities

Communities' identification in topic-focused social media users interaction networks can offer improved understanding of different opinions and interest expressed on a topic. In this paper we present a community detection approach for user interaction networks which exploits both their structural properties and intensity patterns. The proposed approach builds on existing graph clustering methods that identify both communities of nodes, as well as outliers. The importance of incorporating interactions' intensity in the community detection algorithm is initially investigated by a benchmarking process on synthetic graphs. By applying the proposed approach on a topic-focused dataset of Twitter users' interactions, we reveal communities with different features which are further analyzed to reveal and summarize the given topic's impact on social media users.