Exploring Twitter communication dynamics with evolving community analysis

Online Social Networks (OSNs) have been widely adopted as a means of news dissemination, event reporting, opinion expression and discussion. As a result, news and events are being constantly reported and discussed online through OSNs such as Twitter. However, the variety and scale of all the information renders manual analysis extremely cumbersome, and therefore creating a storyline for an event or news story is an effort-intensive task. The main challenge pertains to the magnitude of data to be analyzed. To this end, we propose a framework for ranking the resulting communities and their metadata on the basis of structural, contextual and evolutionary characteristics such as community centrality, textual entropy, persistence and stability. We apply the proposed framework on three Twitter datasets and demonstrate that the analysis that followed enables the extraction of new insights with respect to influential user accounts, topics of discussion and emerging trends. These insights could primarily assist the work of social and political analysis scientists and the work of journalists in their own story telling, but also highlight the limitations of existing analysis methods and pose new research questions. To our knowledge, this study is the first to investigate the ranking of dynamic communities. In addition, our findings suggest future work regarding the determination of the general context of the communities based on structure and evolutionary behavior alone