Chirag Shah


Associate Professor of Library and Information Science


CI 302

Chirag Shah studies interactive information retrieval/seeking, especially those involving social and collaborative aspects. He directs the InfoSeeking Lab, as well as special interest groups on Collaborative Information Seeking, Social Information Seeking, and Sensor-Aware Information Seeking Behavior. Chirag is also an affiliate faculty member in the Department of Computer Science. A competitive ballroom dancer and teacher and an avid traveler, he lives in Somerset with his wife and daughters.


The University of North Carolina at Chapel Hill
Ph.D., Information Science

University of Massachusetts Amherst
M.S., Computer Science

Indian Institute of Technology, India
M.Tech, Computer Technology

Dharamsinh Desai Institute of Technology, India
B.E., Computer Engineering


Chirag Shah is interested in various aspects of information seeking/retrieval, in personal, group, and social contexts. In addition to looking at information being generated and shared in online environments as a part of Information Science, he investigates ways to collect and transform data to meaningful information under the broad label of Data Science. This involves Big Data analytics, mobile and sensor–based data collection, and data mining and machine learning techniques. Finally, he is interested in investigating how data, information, and knowledge influences people’s, organizations’, and communities’ behaviors as a part of what he refers to as Decision Science. He tries to keep a balance between studying systems and users, designing algorithms and theories, and building tools and conducting user studies. You can find more details about Shah's research here.

Research Keywords

Centers, Labs, and Clusters

Funded Projects

NSF award for project titled "Information Fostering - Being Proactive in Information Seeking" (Sept. 2017-Aug. 2020) PI ($499,656), 2017

"Interactive technology for media literacy drug prevention in community groups” from the National Institute of Health (NIH). National Institute for Drug Abuse (NIDA). (May 2017-April 2019; Greene, K. (Co-PI), Hench, M. (Co-PI) & Shah, C. (Co-I); $1,426,420)

IMLS National Leadership Grant with Dr. Rich Gazan of University of Hawaii for project "Online Q&A in STEM Education: Curating the Wisdom of the Crowd" (Sept. 2016-Aug. 2019), PI ($490,973), 2016

Google Research Award with Dr. Vivek Singh for project "Predicting Search Behavior Using Physical and Online Explorations" (Sept. 2016-Aug. 2017), PI ($62,813), 2016

NSF award for project titled "Characterizing and Evaluating Whole Session Interactive Information Retrieval" with Nick Belkin ($499,425), Sept. 2014-Aug. 2017

Early Faculty Career Development Grant from IMLS for project "CIS3: Collaborative Information Seeking Support and Services in Libraries" ($272,996), Sept. 2012-Aug. 2016

NSF's Building Community and Capacity for Data-Intensive Research in the Social, Behavioral, and Economic Sciences and in Education and Human Resources (BCC-SBE/EHR) with Mor Naaman (Rutgers) and Winter Mason (Stevens) for project "Building Communities for Transforming Social Media Research with SOCRATES: SOcial and CRowdsourced AcTivities Extraction System" ($219,126), Sept. 2012-Feb. 2016

Current Projects

Information Fostering: People often have difficulty in expressing their information needs. Many times this results from a lack of clarity for the task at hand, or the way an information or search system works. In addition, people may not know what they do not know. This project will address such problems by investigating the nature of the work a person is doing, predicting the potential problems they may encounter, and providing help to overcome those problems. Such a help could be an object such as a document or a query, a strategy, or a person. This whole process is referred to as Information Fostering. Beyond creating a general-purpose recommender system, Information Fostering is an idea of providing proactive suggestions and help to information seekers. This could allow them avoid potential problems and capture promising opportunities in search before it is too late. More information:

Social Information Seeking (SIS): SIS delineates a process through which users locate, and share information in participatory online forums, such as social media platforms and question-answering Websites. As these services become integral to our daily lives, it is becoming more effective and efficient for people to seek out information through strong or weak connections — either in their social networks or on the open Web. We are studying methods and motivations, as well as content being produced and shared in such social/crowdsourcing environments. More information:

Collaborative Information Seeking (CIS): It is natural for humans to collaborate while dealing with complex problems. We consider this process of collaboration in the context of information seeking. We study issues of CIS from both system and user sides. On one hand, we are developing new tools to support collaboration, and on the other hand we are empirically investigating how people behave in various settings of time, space, and other situations. More information:

People Analytics: People Analytics is a research project that aims to investigate and use various signals generated by people to study their behavior. These signals include, but not limited to, social media data, implicit and explicit actions performed by people online, as well as body sensors logs. The analytics to understand and influence people come from two types of signals: social media, and mobile and wearable devices. More information:

UN Armed Conflict: The goal of this project is to analyze the armed conflict data compiled by the UN since World War I. Two goals drive this study. In the short term, researchers hope to predict the duration of ongoing conflicts in the Middle East, including those in Syria, Egypt, and Iran. In the long term, researchers hope to identify regions that are prone to war—whether it be civil or between states—and predict their involvement in future conflicts. More information:

Extracting intentions in information seeking: This research addresses a newly important issue in contemporary life. As people become more accustomed to using the Web for finding information, they are increasingly using it for addressing ever more complex and personally important information problems. However, current Web search engines have been developed and specifically tuned to helping people find simple, mostly factual information, usually as a single response list to a single, simple query. But when they try to address the new types of problems, people need to engage in longer information seeking episodes than the one query-one response paradigm assumes. They may also need to engage in many activities other than just clicking on a search result, such as reading, evaluating, comparing and using information. Current Web search engines do not sufficiently support this model of information seeking and use. This research addresses this problem by studying why people engage in such complex information seeking (that is, the reasons that motivate them to do this), and what they try to accomplish during the course of an information seeking episode (their search intentions). More information:

Selected Publications

Shah, C. (2017). Social Information Seeking: Leveraging the Wisdom of the Crowd. The Information Retrieval (IR) series. Berlin, Germany: Springer. Available from (9 chapters, 177 pages)

Shah, C., Hendahewa, H., & Gonzalez-Ibanez, R. (2016). Rain or shine? Forecasting search process performance in exploratory search tasks. Journal of the Association for Information Science & Technology (JASIST), 67(7), pp 1607-1623. 

Shah, C., & Leeder, C. (2016). Exploring Collaborative Work Among Graduate Students Through the C5 Model of Collaboration: A Diary Study. Journal of Information Science (JIS), 42(5), pp. 609-629.

Shah, C., and Gonzalez-Ibanez, R. (2011, July 24-28). Evaluating the synergic effect of collaboration in information seeking. Proceedings of ACM SIGIR, pp. 913-922. Beijing, China. 

Shah, C., & Pomerantz, J. (2010, July 19-23). Evaluating and predicting answer quality in community QA. Proceedings of ACM SIGIR 2010 Conference. Geneva, Switzerland, 411-418.

Awards & Recognitions

Chancellor’s Scholar (Since 2017)

Best Student Paper award at ACM/IEEE Joint Conference on Digital Libraries (JCDL) conference, 2016

James M. Cretsos Leadership Award at ASIST, 2013

Best Paper Award in the Political Communication category at the International Communication Association (ICA) annual meeting, 2011

Best Political Science Software Award to ContextMiner by American Political Science Association, 2010

Best paper award at ACM SIGIR conference, 2008