報告題目：Unexpectedly high capacity to spread information and extremely imbalanced discursive power of social media networks
摘要：Online social networks have emerged as an important medium for the spread of information and have been used in various fields, from political campaign to marketing, disaster relief and social sensing. All of these applications rely on how information spreads on social media networks. Most studies assume that information spreading is a percolation process and large cascades occur only when the retweet probability of information items exceeds the percolation critical point, also known as tipping point. However, whether this widely used hypothesis is valid in current large-scale social media remains unclear. Here we continuously observe at least 0.18 million users’ online behaviors for three years in Weibo, the biggest microblog social medium in China, crawl almost the whole friendship network of 100 million users and collect a large number of information tracks within the same period of time. We find that the cascading threshold is only one tenth of that theoretically obtained previously, and 98.4% of the information items that have led to outbreaks in real social media could be incorrectly predicted to be at subcritical states by the existing theories. This finding indicates that the capacity of social media to spread information has been seriously underestimated. Moreover, the positive-feedback effect in the coevolution between user activity and net-work structure, on both Weibo and Twitter, becomes stronger with time. Such a stronger effect induces extreme imbalance in users’ discursive power. Indeed, we find that the top 0.7% of users possess 99.3% of discursive power, 17 times more serious than previous theoretical prediction. We incorporate the coevolution mechanism into network percolation theory, offering a novel model that agrees with empirical data much better than previous ones. Taken together, our results deepen the understanding of phase transition and coevolution dynamics in social media, applicable to a wide range of problems pertaining to information cascades on networks.
博士，現在為中山大學數據與計算機學院副教授（中大百人計劃），博士生導師。2011年畢業于北京師范大學系統科學學院，獲得系統理論方向理學博士學位，并獲得北京市優秀博士論文獎；2011-2013年紐約城市大學Levich Institute 博士后。近幾年主要從事具有圖或者網絡結構的大數據挖掘與人工智能算法與理論研究工作，探索數據背后的自然物理規律。發表論文 40 余篇，其中通訊、第一作者論文 25 篇，包括 Nature Physics, PRL, PRX 各 1 篇，PNAS 2 篇，PRE 11 篇，其中 Nature Physics 論文被選入該期封面推薦論文。