Measuring the External Influence in Information Diffusion

Information flow in social network is assumed to be transmitted from node to node through the edges of network. In real world, however, people are influenced not only by local social neighbors but also by out-of-network services and sources, such as mass media and external websites. As a consequence, in addition to spreading by social edges, information can also reach a long-distance node by "jumping" cross the network. Then one of important issues coming out of the phenomenon is: how do these external services affect the diffusion process in social network? In this paper we develop an algorithm which allows us to distinguish the effects of external influence in diffusion process. By applying the algorithm to millions of diffusion cascades, we find that, although only a small portion of reshare activities arise from external influence directly, external influence plays a significant role in information diffusion. In particular, external influence affects nearly 50% to 70% of cascade node in average, and the effects become stronger as the cascade becomes larger. In addition, we characterize external influence as two categories, and show that one category mainly affects the size of diffusion tree and the other focuses on affecting the depth. Finally, we find that, due to the external services, the influentials become less important and more large cascades can be triggered by ordinary people. Together, these observations suggest new directions for modeling diffusion process and constructing more useful viral marketing strategy.