Big Data Driven Information Diffusion Analysis and Control in Online Social Networks
Thanks to recent advance in massive social data and increasingly mature big data mining technologies, information diffusion and its control strategies have attracted much attention, which play pivotal roles in public opinion control, virus marketing as well as other social applications. In this paper, relying on social big data, we focus on the analysis and control of information diffusion. Specifically, we commence with analyzing the topological role of the social strengths, i.e., tie strength, partial strength, value strength, and their corresponding symmetric as well as asymmetric forms. Then, we define two critical points for the cascade information diffusion model, i.e., the information coverage critical point (CCP) and the information heat critical point (HCP). Furthermore, based on the two real-world datasets, the proposed two critical points are verified and analyzed. Our work may be beneficial in terms of analyzing and designing the information diffusion algorithms and relevant control strategies.