Computing That Serves

Colloquium: Mining Social Media Data: Some Lessons Learned


Thursday, November 5, 2015 - 11:00am


Huan Liu


Christophe Giraud-Carrier

Mining Social Media Data: Some Lessons Learned
Thursday, November 5, 2015
1170 TMCB

People increasingly use social media for communications and networking.  Their active participation in numerous and diverse online activities continually generates massive amounts of social media data. This undoubtedly big data offers novel opportunities to understand human behavior, and introduces new challenges, e.g., how to find behavioral patterns, how to ensure that patterns are valid when no ground truth is available, and how we assess the quality of sampled social media data. We will show the intricacies of social media data, present original problems and methods to illustrate challenges and opportunities of mining social media data.


Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in EECS at Shanghai Jiao Tong University. He was recognized for excellence in teaching and research in Computer Science and Engineering at Arizona State University. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating problems that arise in real-world applications with high-dimensional data of disparate forms. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He serves on journal editorial/advisory boards and numerous conference program committees. He is a Fellow of IEEE and a member of several professional societies.