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Welcome to my homepage. My name is Yu Chi.

I am a 4th year Ph.D. student at School of Computing and Information, University of Pittsburgh, and a member of Information Retrieval, Integration and Synthesis Lab, which is led by my advisor Prof. Daqing He. I also work with Dr. Leanne Bowler from Pratt Institute, and Dr. Tingting Jiang from Wuhan University. Learn more about me from my resume.

You are also welcome to contact me via yuc73@pitt.edu.

In general, I use both qualitative and quantitative methods to explores how web search could support users to achieve their information needs and promote fairness in education and healthcare. My research interests include:

  • Human Information Behavior
  • Human-Computer Interaction
  • User Experience
  • Health Information Seeking
  • Web Analytics
  • Learn more about my research from my research projects.

    Research Projects

    [Understanding and supporting search as learning in online health information seeking]

    In particular, I am interested in examining how and how well laypeople learn about health-related knowledge in parallel with the health information seeking process through web search. It has long been understood that knowledge acquisition is an important component in the information seeking process. Further, empirical studies have demonstrated that learning is a common phenomenon in information seeking. However, for users, especially laypeople, who must gain knowledge through their interactions with a search engine, the current general-purpose search engine does not sufficiently support learning through search. Health information seeking (HIS, hereafter) is a domain-specific search, where users who possess higher knowledge tend to have better strategies and performances in solving their search tasks. While learning clearly plays an important role in the HIS process, there has been little research in this area. Little is known about the factors that might enhance or impede such learning during online HIS. Therefore, this project aims at examining health consumers, especially laypeople's search as learning behaviors and performances. A mixed method design is adopted, consisting of experimental-based studies and interviews. So far, we have conducted 24 user studies and semi-structured interviews, investigating the source selection behaviors in the HIS tasks with increasing levels of learning goals. The results of this phase of the study will be used to guide the following analysis and predict laypeople's knowledge levels in the HIS process and provide corresponding support.

    Published Work:

  • [Chi, Y., He, D., Han, S., & Jiang, J. (2018, March). What Sources to Rely on: Laypeople’s Source Selection in Online Health Information Seeking. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (pp.233-236). ACM. (CHIIR’18) [PDF]
  • Results of the interview have been submitted to a conference and under review.
  • Another work predicting the learning performance based on search behaviors is in preparation.

    [Analyzing user behavior in virtue of clickstream data analysis]

    Another research interest of mine is to explore different methods or frameworks that can be applied to the analysis of human's interaction with websites. It is common to study user behavior based on log analysis. Most previous studies, however, heavily rely on search logs. The problem is that search log analysis, consisting of the analysis of users’ terms, queries, and sessions, is only applicable to examine the usage of web search engines. Clickstream data offers an unobtrusive data source for understanding web users’ information behavior beyond searching. However, it remains underutilized due to the lack of structured analysis procedures. Based on our experience from two case studies with clickstream data analysis, we integrated and proposed an analysis framework. Overall, the integrated analysis framework is designed to be independent of any specific research settings so that it can be easily adopted for different clickstream datasets and research questions.

    Published Work:
  • Chi, Y., Jiang, T., He, D., & Meng, R. (2017). Towards an integrated clickstream data analysis framework for understanding web users’ information behavior. (iConference’17, 34% acceptance rate, Oral) [PDF]
  • Jiang, T., Chi, Y. & Gao, H. (2017). A clickstream data analysis of Chinese academic library OPAC users’ information behavior. Library & Information Science Research, 39(3), 213-223. [PDF]
  • [Exploring and designing teens' data worlds in the public library]

    As the research assistant of the Youth Data Literacy project , I assisted and led workshops, interviews and observations with teens and staffs at seven branches of the Carnegie Library of Pittsburgh to examine teen data literacy practices and design for appealing data environment. I also led a subproject, exploring the interplay between teens’ Affective states (A), Behavioral states (B), and Cognitive states (C) in relation to the personal data they generate in social media, applying the “ABC model” from the social psychology domain. Given the results, we suggest librarians, educators and software developers apply a range of strategies in reaction to teens’ different ABC states to the design of data literacy programs, services, and software applications. Published Work:

  • Chi, Y., Jeng, W., Acker, A., & Bowler, L. (2018, March). Affective, Behavioral, and Cognitive Aspects of Teen Perspectives on Personal Data in Social Media: A Model of Youth Data Literacy. In International Conference on Information (pp. 442-452). Springer, Cham. (iConference’18, 30% acceptance rate, Oral) [PDF]
  • Bowler, L., Acker, A., Jeng, W., & Chi, Y. (2017). “It lives all around us”: Aspects of data literacy in teen’s lives. Proceedings of the Association for Information Science and Technology, 54(1), 27-35. (ASIS&T’17) [PDF]
  • Publications

    [Conference Proceedings]
  • [C10]Chi, Y., Jeng, W., Acker, A., & Bowler, L. (2018, March). Affective, Behavioral, and Cognitive Aspects of Teen Perspectives on Personal Data in Social Media: A Model of Youth Data Literacy. In International Conference on Information (pp. 442-452). Springer, Cham. (iConference’18, 30% acceptance rate, Oral) [PDF]
  • [C09]Chi, Y., He, D., Han, S., & Jiang, J. (2018, March). What Sources to Rely on: Laypeople’s Source Selection in Online Health Information Seeking. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (pp.233-236). ACM. (CHIIR’18) [PDF]
  • [C08]Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., & Chi, Y. (2017). Deep Keyphrase Generation. 55th Annual Meeting of Association for Computational Linguistics. (ACL’17, 25% acceptance rate). [PDF]
  • [C07]Chi, Y., Jiang, T., He, D., & Meng, R. (2017). Towards an integrated clickstream data analysis framework for understanding web users’ information behavior. (iConference’17, 34% acceptance rate, Oral) [PDF]
  • [C06]Bowler, L., Acker, A., Jeng, W., & Chi, Y. (2017). “It lives all around us”: Aspects of data literacy in teen’s lives. Proceedings of the Association for Information Science and Technology, 54(1), 27-35. (ASIS&T’17) [PDF]
  • [C05]Han, S., He, D., & Chi, Y. (2017) Understanding and modeling behavior patterns in cross- device web search. Proceedings of the Association for Information Science and Technology, 54(1), 150-158. (ASIS&T’17, Oral) [PDF]
  • [C04]Meng, R., Zhao, Z., Chi, Y., & He, D. (2017). Automatic Course Website Discovery from Search Engine Results. iConference 2017 Proceedings Vol. 2. [PDF]
  • [C03]Li, L., He, D., Zhang, D., Chi, Y., & Zhang, C. (2017). Types of Tags for Annotating Academic Blogs.iConference 2017 Proceedings. [PDF]
  • [C02]Meng, R., Lu, W., Chi, Y., & Han, S. (2017). Automatic classification of citation function by new linguistic features. iConference 2017 Proceedings. [PDF]
  • [C01]Chi, Y., Han, S., He, D., & Meng, R. (2016, January). Exploring knowledge learning in collaborative information seeking process. In Search as Learning workshop @ SIGIR’16. [PDF]

  • [Journal Articles]
  • [J05]Jeng, W., He, D., & Chi, Y. (2017). Social science data repositories in data deluge: A case study of ICPSR’s workflow and practices. The Electronic Library, 35(4), 626-649. [PDF]
  • [J04]Jiang, T., Chi, Y. & Gao, H. (2017). A clickstream data analysis of Chinese academic library OPAC users’ information behavior. Library & Information Science Research, 39(3), 213-223. [PDF]
  • [J03]Jiang, T., Liu, F., & Chi, Y. (2015). Online information encountering: modeling the process and influencing factors. Journal of documentation, 71(6), 1135-1157. [PDF]
  • [J02]Jiang, T., Chi, Y., & JIA, W. (2015). Exploring users’ within-site navigation behavior: A case study based on clickstream data. Journal of Data and Information Science, 7(4), 63-76.
  • [J01]Jiang, T., Chi, Y., & Shi, M. (2013). Information Seeking in Social Tagging Systems: An Empirical Survey of Douban Users. Library and Information Service, 57(21), 112-118.