主 題：Blog Communities and Influential Bloggers
報告人; Huan Liu
Associate Professor
Department of Computer Science & Engineering
Arizona State University, USA
時 間： 6月20日 上午10:00-11:00
地 點： 南京大學蒙民伟楼404會議室
主持單位：IEEE Nanjing Section 计算机分会
南京大學计算机科学与技术系
机器学习与数据挖掘研究所
Title: Blog Communities and Influential Bloggers
Blogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. Bloggers form their virtual communities of similar interests. Activities happened on Blogosphere affect the external world. One way to understand the development on Blogosphere is to find influential blog sites. There are many non-influential blog sites which form "the long tail". Regardless of a blog site being influential or not, there are influential bloggers. Inspired by the high impact of the influentials in a physical community, we study a novel problem of identifying influential bloggers at a blog site. Active bloggers are not necessarily influential. Influential bloggers can impact fellow bloggers in various ways. In this work, we discuss the challenges of identifying influential bloggers, investigate what constitutes influential bloggers, present a preliminary model attempting to quantify an influential blogger, and pave the way for building a robust model that allows for finding various types of the influentials. To illustrate these issues, we conduct experiments with data from a real-world blog site, evaluate multi-facets of the problem of identifying influential bloggers, and discuss unique challenges. We conclude with interesting findings and future work.
Short Bio:
Huan Liu is an associate professor of Computer Science and Engineering at Arizona State University. He received his Ph.D. from University of Southern California, researched at Telecom Research Labs in Australia, and taught at National University of Singapore. He has been a visiting scholar at Microsoft Research Asia, a consultant of AOL, a summer faculty fellow at Motorola Research Lab and at Air Force Research Lab, and was invited to Google Faculty Summit (Summer 2008). His research interests are in data/web mining, machine learning, social computing, and artificial intelligence. These include search and optimization problems that arise in many real-world applications with high-dimensional data of disparate forms such as text categorization, streaming data summarization, biomarker identification, and text/web mining. His research covers efficient search algorithms, semi-supervised models, spectral analysis methods, bias analysis, and experiment and evaluation methodologies. The research is being expanded to taxonomy-based group profiling, searching for influential bloggers in a community, information integration of multiple data sources, trust and reputation of multi-source information, and predicting high-cost patients in healthcare domains. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. His former graduate students have been professors at research universities and employed by Amazon, Google, Microsoft, and Yahoo, among others. He is a co-organizer of the International Workshop Series on Social Computing, Behavioral Modeling, and Prediction in Phoenix, AZ (SBP’08 and SBP’09), a conference co-chair of the 12th Pacific Asia Conference on Knowledge Discovery and Data Mining in Osaka, Japan (PAKDD’08), and a program committee co-chair of the SIAM International Conference on Data Mining (SDM’09 http://www.siam.org/meetings/sdm09 ) in Reno Area, Nevada.
