報告人: Prof. Benyuan Liu
Department of Computer Science, University of Massachusetts Lowell
報告時間：6月20日 星期四 下午2:30
Seasonal influenza epidemics causes severe illnesses and 250,000 to 500,000 deaths worldwide each year. Other pandemics like the 1918 “Spanish Flu” may change into a devastating one. Reducing the impact of these threats is of paramount importance for health authorities, and studies have shown that effective interventions can be taken to contain the epidemics, if early detection can be made. We introduce the Social Network Enabled Flu Trends (SNEFT), a continuous data collection framework which monitors flu related tweets and track the emergence and spread of an influenza. We show that text mining significantly enhances the correlation between the Twitter/Facebook data and the Influenza like Illness (ILI) rates provided by Centers for Disease Control and Prevention (CDC). For accurate prediction, we implemented an auto-regression with exogenous input (ARX) model which uses current Twitter/Facebook data, and CDC ILI rates from previous weeks to predict current influenza statistics. Our results show that, while previous ILI data from CDC offer a true (but delayed) assessment of a flu epidemic, Twitter/Facebook data are highly correlated with the ILI rates across different regions within USA and can be used to effectively improve the accuracy of our prediction. Therefore, Twitter/Facebook data provides a real-time assessment of the current epidemic condition and can act as supplementary indicator to gauge influenza within a population and helps discovering flu trends ahead of CDC.
Dr. Benyuan Liu is an Associate Professor in the Department of Computer Science at the University of Massachusetts Lowell. He received his Ph.D. degree in computer science from the University of Massachusetts Amherst. Prior to that, he received his B.S. degree in physics from University of Science and Technology of China (USTC) and M.S. degree in physics from Yale University. Dr. Liu's main research interests are in the area of application, algorithm design and performance analysis of various computer networking technologies. His research has been published in premium computer networking conferences such as ACM Mobicom, ACM Mobihoc and IEEE Infocom, and journals such as IEEE/ACM Transactions on Networking (ToN), IEEE Transactions on Mobile Computing (TMC), IEEE Transactions on Parallel and Distributed Systems (TPDS), and IEEE Journal of Selected Areas in Communications (J-SAC). His research has been supported by the National Science Foundation (NSF), National Institutes of Health (NIH), and Microsoft Research. He is a recipient of the NSF CAREER Award. Dr. Liu served as program co-chair of International Conference on Wireless Algorithms, Applications, and Systems (WASA 2009), track co-chair of IEEE ICCCN 2008, and program committee member of numerous networking conferences.