차미영 (KAIST / 부교수)
Computational Social Science
Social media and blogging services have become extremely popular. Every day hundreds of millions of users share conversations on random thoughts, emotional expressions, political news, and social issues. Users interact by following each other's updates and passing along interesting pieces of information to their friends. Information therefore can diffuse widely and quickly through social links. Information propagation in networks like Twitter and Facebook is unique in that traditional media sources and word-of-mouth propagation coexist. The availability of digitally-logged propagation events in social media help us better understand how user influence, tie strength, repeated exposures, conventions, and various other factors come into play in the way people generate and consume information in the modern society. Collectively, the rich data allows for computationally solving complex social science problems. In this talk, I will present several research directions towards computational social science, including rumor detection, price nowcasting, and alleviating depressive moods via social media.
Meeyoung Cha is an associate professor at the School of Computing in KAIST and a visiting professor at Facebook. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook's Data Science Team as a Visiting Professor for a year.