Deepening Our Understanding of Social Media via Data Mining

Huan Liu

Vendredi 30 mai 2014 à 11h, salle 25-26/101


Social media mining differs from traditional data mining in many ways, offering unique opportunities to advance data mining. It consists of massive amounts of user-generated content and extensive networked data. As detailed in our latest textbook “Social Media Mining: An Introduction”, we face novel challenges such as “the evaluation dilemma” and “the noise removal fallacy”. We will introduce these challenges and present some recent research issues we encounter – a big-data paradox unique to social media where many social networking sites are present but only minimum information is available, and whether distrust is relevant and useful in social media mining.  We will exemplify the intricacies of social media data, and show how to exploit unique characteristics of social media data in developing novel algorithms and tools for social media mining. The textbook’s pdf free download is at   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 CSEE at Shanghai JiaoTong University. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 President’s Award for Innovation. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating interdisciplinary problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He is a co-author of Social Media Mining: An Introduction by Cambridge University Press. He serves on journal editorial boards and numerous conference program committees, is an IEEE Fellow and a member of several professional societies.