Eyes of the Foreseer: Integrative and In Situ Information Retrieval and Mining in Online Communities

eyes of the foreseer An NSF Career Award to study 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. To know more

Assessing Information Credibility without Authoritative Sources

rumorsThis project will develop tools that help people make personal assessments of credibility. Rather than relying on particular sources as authoritative arbiters of ground truth, the goal is to minimize the amount of “social implausibility”. That is, the tool will identify assertions that are disbelieved by “similar” people (those who, after careful consideration, someone tended to agree with in the past) or come from sources that someone has tended to disagree with. A text mining system for online media will be developed to extract controversial assertions and the beliefs expressed by users about those assertions. Comparisons of beliefs about common assertions, and retractions or updates to beliefs, will be tracked as part of personalized reputation measures. To know more

Motivation Classification for Pro-Social Lending

To understand lender motivations on, an online microfinance site, we classify the lenders’ self-stated motivations
into ten categories using human coders and trained classifiers. We employ both supervised and semi-supervised machine learning methods and show that machine-learning-based text classification methods are effective for the task of classifying motivations, yet it is much more challenging than traditional topic-based categorization. To know more …

Developing an Intelligent and Socially Oriented Search Query Recommendation Service for Facilitating Information Retrieval in Electronic Health Records


This project will develop a search query recommendation service for health practitioners and researchers that will help professionals retrieve information from electronic health records more easily and efficiently when using full-text search engines. This recommendation service will also allow users to develop and refine search queries for more effective searches in the future.

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Wordsmith in the Cloud: Refining Language Models using Web-Scale Language Networks

compute in cloudIn this project, cloud computing and novel map-reduce algorithms will be employed to extract heterogeneous language networks from Web-scale text collections. These language networks will be used to smooth and contextualize language models in various domains, making them accurate and robust. The refined language models will help improve state-of-the-art text retrieval and mining techniques, enhancing the information access and knowledge acquisition experience of real users across community and language boundaries. To know more

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