Focal Structures Behavior in Dynamic Social Networks

The expansion of coordinating communities via focal information spreaders on online social networks has attained much-needed attention over the past few years. Several methods have been applied to investigate the influential communities of information spreaders in static social networks. However, investigating static social networks does not entirely reflect the activities and the dynamics of evolving communities over time. Researchers have applied advanced operational methods such as game theory and evolving complex graphs to describe the change in the regular communities in dynamic social networks. Yet, these methods need the ability to describe the focal information spreaders in dynamic social networks. For this purpose, in this research, we propose a systematic approach to measure the influence of focal information spreaders and track their evolution in social networks over time. This novel approach combines the focal structure analysis model and the adaptation algorithm to identify the coordinating communities of information spreaders in social networks and illustrate their development in the network over time, respectively. We evaluate our findings using a real-world dynamic Twitter network collected from the Saudi Arabian women’s Right to Drive campaign coordination in 2013. The outcomes of this approach allow observing, predicting, tracking, and measuring the coordination among the focal information spreaders over time. Correspondingly, this approach investigates and illustrates when the information spreaders will escalate their activities, where they concentrate their influence in the network, and what coordinating communities of spreaders are more tactical than others in the network.