题  目：Detecting and modeling real percolation phase transition of information spreading on social media

内容简介：Most important applications on social media rely on how users spread information. A widely used hypothesis is that information spreading is a percolation process, which connects popular social media to phase transitions in theoretical physics and leads to many impactful results. However, the hypothesis has not been verified, since the phase transition has never been directly observed in any social media. Through analyses of 100 million user’s data in Weibo, we detect the phase transition and find the empirical threshold is much smaller than theoretical prediction. Our systematic analyses of users' behaviors reveal a positive feedback in the coevolution between network structure and user activity level, which is the key reason for the lower threshold. Moreover, this coevolution induces an extreme imbalance in users’ influence.Our findings indicate the spreading power of social media is much higher than expected and we need to reexamine many information spreading problems.

报告人：中山大学  胡延庆  副教授

报告人简介：Yanqing Hu received his PhD degree from Beijing Normal University in 2011. He was a Postdoctoral Researcher at the Levich Institute, City University of New York, from 2011 to 2013. Currently, he is an Associate Professor in the school of data and computer science at Sun Yat-sen University. His current research interests mainly focus on using big data to explore the mechanisms inside complex systems, such as the spreading of human behavior, the resilience of brain and infrastructure networks, the network structure predictability and formation process, etc. 

时间：2021年1月22日（周五）下午15：30∼17：00

地点：南海楼507室

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信息科学技术学院

2021年1月22日