Ultra-efficient information detection on large-scale online social networks

Abstract

As a powerful and low-cost instant information dissemination platform, large-scale online social networks (OSNs) play a pivotal role in shaping our modern information age. The efficient detection of wide-spreading information in OSNs is very important in many aspects including public opinion supervision, social governance, stock markets, counter-terrorism and presidential election. However, real-world OSNs have gigantic sizes and thus their full structural data are usually unavailable, making this problem extremely challenging. In this work, we illustrate the close mapping between efficient detection and optimal spreading from the perspective of network percolation theory. This analogy inspires us to propose a theory of using only limited local network information to select the optimal set of information sensors. Through extensive simulations on both synthetic and real-world networks, we find that for networks with theoretically infinite size, only a finite and small number of sensor nodes are needed to detect the global spreading information with almost certainty. Most importantly, we empirically confirm the utility of our theory on the largest micro blog in China by crawling almost the full Sina Weibo social network with 99,546,027 users in 2014 and the real spreading data of Weibo messages.