CAREER: Eyes of the Foreseer - Integrative and In Situ Information Retrieval and Mining in Online Communities
National Science Foundation Award Number: NSF-IIS 1054199
School of Information
Department of Electrical Engineering and Computer Science
University of Michigan
Office: 4437 North Quad, 105 S. State St., Ann Arbor, MI 48109
Email: qmei AT umich DOT edu
- Qiaozhu Mei. Principal Investigator.
- Rong Xin. PhD Student. School of Information, University of Michigan.
- Tao Sun. Visiting PhD Student. School of EECS, Peking University.
- Jian Tang. Visiting PhD student. School of EECS, Peking University.
- Yue Wang. PhD Student. Department of EECS, University of Michigan.
- Zhe Zhao. PhD Student. Department of EECS, University of Michigan.
This website is based upon work supported by the National Science Foundation under Grant No. IIS-1054199. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
- Award Number: IIS-1054199
- Duration: January 15, 2011 to December 31, 2015
- Award Amount: $449,531
- Award title: CAREER: Eyes of the Foreseer - Integrative and In Situ Information Retrieval and Mining in Online Communities
With the growth of online communities, the Web has evolved from networks of shared documents to networks of knowledge-sharing groups and individuals. A vast amount of heterogeneous yet interrelated information is being generated for which existing information analysis techniques are inadequate. Current tools often neglect the actual creators and consumers of information, and as a result, the findings are only useful to data analysts.
The user-centric Foreseer is the next generation of information analysis for online communities. It represents a new paradigm of study through the four "Cs": content, context, crowd, and cloud. Information analysis of content is put into the context of the users’ daily lives to benefit the communities (crowd) that generate information residing in the cloud. This project is the first integrative and in situ analysis of information generated in online communities that is of the people, by the people, and for the people. Research of Foreseer consists of formal community models, efficient data analysis tools, advanced solutions of real applications, and novel information systems.
Making the results available to everyday Web users, not just data analysts, will result in improved dissemination of ideas, shared public opinions, and wise decision-making in online communities. Novel Web-based information systems will form prototypes that can be used in online social and health communities. The research will enhance the current information analysis and retrieval curricula and lead to a number of new classes in information science and health informatics.
Part of research results in this project have been used in information retrieval courses (SI 650/EECS 549), network courses (SI 508), and data mining (SI 721) offered by the school of information. Online competitions through Kaggle-in-Class have been established based on the research of this project. Through the online competitions students have access to a new, in-situ classroom learning model of data analysis techniques.
- Jian Tang, Zhaoshi Meng, Xuanlong Nguyen, Qiaozhu Mei, and Ming Zhang, "Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis," in Proceedings of the 31st international conference on Machine learning (ICML'14), forthcoming.
- Tianyi Lin, Wentao Tian, Qiaozhu Mei, and Hong Cheng, "The Dual-Sparse Topic Model: Mining Focused Topics and Focused Terms in Short Text," in Proceedings of the 23rd International World Wide Web Conference (WWW'14), forthcoming.
- Xin Rong and Qiaozhu Mei, "Diffusion of Innovations Revisited: from Social Network to Innovation Network," in Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM'13), pp. 499-508, 2013. (17% acceptance)
- Jian Tang, Ming Zhang, and Qiaozhu Mei, "One theme in all views: modeling consensus topics in multiple contexts," in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'13), pp. 5-13, 2013. (17% acceptance)
- Tao Sun, Ming Zhang, and Qiaozhu Mei, "Unexpected relevance: an empirical study of serendipity in retweets," in Seventh International AAAI Conference on Weblogs and Social Media (ICWSM'13), 2013. (20% acceptance)
- Zhe Zhao and Qiaozhu Mei, "Questions about questions: an empirical analysis of information needs on Twitter," in Proceedings of the 22nd international conference on World Wide Web (WWW'13), pp. 1545-1556, 2013. (15% acceptance)
- Jingrui He, Hanghang Tong, Qiaozhu Mei, and Boleslaw Szymanski, "GenDeR: A Generic Diversified Ranking Algorithm," in Advances in Neural Information Processing Systems 25 (NIPS'12), pp. 1151-1159, 2012.
- Lei Yang, Tao Sun, Ming Zhang, and Qiaozhu Mei, "We know what@ you# tag: does the dual role affect hashtag adoption?," in Proceedings of the 21st international conference on World Wide Web (WWW'12), pp. 261-270, 2012.
- Yang Liu, Roy Chen, Yan Chen, Qiaozhu Mei, and Suzy Salib, "I loan because...: understanding motivations for pro-social lending," in Proceedings of the fifth ACM international conference on Web search and data mining (WSDM'12), pp. 503-512, 2012.
- Cindy Xide Lin, Qiaozhu Mei, Jiawei Han, Yunliang Jiang, and Marina Danilevsky, "The joint inference of topic diffusion and evolution in social communities," in 2011 IEEE 11th International Conference on Data Mining (ICDM'11), pp. 378-387, 2011.
- Lei Yang, Qiaozhu Mei, Kai Zheng, and David Hanauer, "Query Log Analysis of an Electronic Health Record Search Engine," in Proceedings of the Annual Symposium of American Medical Informatics Association (AMIA'11), pp. 915-924, 2011.
- Daniel Xiaodan Zhou, Paul Resnick, and Qiaozhu Mei, "Classifying the Political Leaning of News Articles and Users from User Votes," in Fifth International Conference on Weblogs and Social Media (ICWSM'11), 2011.
We have established collaboration with Twitter.com, Yahoo!, Kiva.org, IBM, Haodf.com, Tsinghua University, and Peking University in the scope of this project. Thanks to our excellent collaborators, We have got access to real datasets and applications through the collaborations.
Last Updated: December 2013.