报告题目: Geotagging Social Media for Enhanced Location-based Search

报告人：Yan Huang 教授

邀请人：林学民

报告时间：2017年6月22日 15:00-16:00

报告地点：中北校区数学馆201

报告主持人：姚俊杰

报告人简介：

Yan Huang (PhD, University of Minnesota) received her B.S. degree in Computer Science from Peking University, Beijing, China, 1997 and Ph.D. degree in, Computer Science from University of Minnesota, USA, 2003. She is currently a professor at the Department of Computer Science and Engineering and the Associate Dean for Research and Graduate Studies at College of Engineering of the University of North Texas, Denton, TX, USA. Her research interests include machine learning and data mining especially from big geo-referenced datasets such as social media and transportation data. She has been a visiting scholar of Microsoft Research Asia May – August 2011. During Fall 2011, she visited Fudan University, China. Currently, she is on the Board of Directors of The SSTD Endowment (2014-2019), is the general chair of SSTD  2017, was the General Chair of ACM SIGSPATIAL 2014 and 2015, and on the Executive Committee of ACM SIGSPATIAL (2010-2014). She received Distinguished Service Award from ACM SIGSpatial in 2010. Her research has been/is supported by Texas Advanced Research Program (ARP), Oak Ridge National Lab, National Science Foundation, Texas Department of Transportation, and U.S. Department of Defense.

报告摘要:

The number of worldwide social network users is expected to reach 2.5 billion by 2018 (1/3 of Earth’s population). A tremendous amount of information is being shared everyday on social media sites.  This massive popularity has lends itself to event detection (social gatherings, natural disaster occurrences, and insurgent activities). Spatiotemporal analytics from social media can assist assimilating social media information of interest in targeted geographic regions and to staying informed about emerging issues. We present our work in the area of generating spatiotemporal analytics in terms of geotagging, location recognition, spatiotemporal correlation detection, and event detection from big social media data and outline challenges and opportunities.