Quantifying Structural Patterns of Information Cascades

Information cascades are ubiquitous in both physical society and online social media, taking on large variations in structures, dynamics and semantics. Although there has been much progress on understanding the dynamics and semantics of information cascades, little is known about their structural patterns. In this paper, we explore a large-scale dataset including 432 million information cascades with explicit records of spreading traces. We find that the structural complexity of information cascades is far beyond the previous conjectures. We first propose seven-dimensional metrics, which reflect size and spreading orientation aspects, to quantify the structural characteristics of millions of information cascades. Further, we analyze the correlations of these metrics, finding some brand new structure patterns of information cascades, potentially providing insights into intrinsic mechanisms governing information spreading in nature and new models to forecast as well as to impose good control over information cascades in real applications.