Faculty Profile

Heejun Kim

Title
Assistant Professor
Department
Information Science
College
College of Information

    

Education

PhD, University of North Carolina at Chapel Hill, 2019.
Major: Information and Library Science
Dissertation Title: Credibility assessment of health information on social media: Discovering credibility factors, operationalization, and prediction
MS, University of Illinois at Urbana-Champaign, 2013.
Major: Geography
Degree Specialization: Geographic Information Science
Dissertation Title: Credibility assessment of volunteered geographic information for emergency management: A model based on Bayesian networks
BS, Yonsei University, 2003.
Major: Electrical Engineering

Current Scheduled Teaching*

DTSC 3020.001, Introduction to Computation with Python, Summer 2024
INFO 5770.001, Introduction to Health Data Analytics, Summer 2024
INFO 5771.020, Applications of Health Data Analytics, Spring 2024
INFO 5707.021, Data Modeling for Information Professionals, Spring 2024
INFO 6950.711, Doctoral Dissertation, Spring 2024
DTSC 3020.800, Introduction to Computation with Python, Spring 2024 Syllabus

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching*

INFO 6950.029, Doctoral Dissertation, Fall 2023
INFO 5770.020, Introduction to Health Data Analytics, Fall 2023 SPOT
INFO 5770.021, Introduction to Health Data Analytics, Fall 2023 SPOT
INFO 6950.766, Doctoral Dissertation, Summer 10W 2023
INFO 3020.001, Introduction to Computation with Python, Summer 8W1 2023 Syllabus SPOT
INFO 5717.001, Networked Data Modeling and Processing, Summer 8W1 2023 Syllabus SPOT
INFO 6950.711, Doctoral Dissertation, Spring 2023
INFO 3020.001, Introduction to Computation with Python, Spring 2023 Syllabus SPOT
INFO 3020.020, Introduction to Computation with Python, Spring 2023 Syllabus SPOT
INFO 3020.800, Introduction to Computation with Python, Spring 3W1 2023 Syllabus SPOT
INFO 5960.020, Library and Information Sciences Institute or Seminar, Spring 2023 Syllabus SPOT
INFO 6660.026, Readings in Information Science, Spring 2023
INFO 6900.029, Special Problems, Spring 2023
INFO 6950.029, Doctoral Dissertation, Fall 2022
INFO 3020.020, Introduction to Computation with Python, Fall 2022 Syllabus SPOT
INFO 5770.020, Introduction to Health Data Analytics, Fall 2022 Syllabus SPOT
INFO 6660.029, Readings in Information Science, Fall 2022
INFO 6900.029, Special Problems, Fall 2022
INFO 6910.021, Special Problems, Fall 8W2 2022
INFO 3020.001, Introduction to Computation with Python, Summer 8W2 2022 Syllabus SPOT
INFO 3020.002, Introduction to Computation with Python, Summer 8W2 2022 Syllabus SPOT
INFO 6950.711, Doctoral Dissertation, Spring 2022
INFO 3020.002, Introduction to Computation with Python, Spring 2022 Syllabus SPOT
INFO 5960.003, Library and Information Sciences Institute or Seminar, Spring 2022 Syllabus SPOT
INFO 6900.710, Special Problems, Spring 2022
INFO 6900.715, Special Problems, Spring 2022
INFO 3020.002, Introduction to Computation with Python, Fall 2021 Syllabus SPOT
INFO 5770.002, Introduction to Health Data Analytics, Fall 2021 Syllabus SPOT
INFO 6660.714, Readings in Information Science, Fall 2021
INFO 3020.202, Introduction to Computation with Python, Summer 10W 2021 Syllabus SPOT
INFO 3020.002, Introduction to Computation with Python, Spring 2021 Syllabus SPOT
INFO 5960.202, Library and Information Sciences Institute or Seminar, Spring 2021 Syllabus SPOT
INFO 5900.851, Special Problems, Spring 2021
INFO 6900.708, Special Problems, Spring 2021
INFO 3020.202, Introduction to Computation with Python, Fall 2020 Syllabus SPOT

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Publications

Published Intellectual Contributions

Conference Proceeding
Huh-Yoo, J., Montgomery, M., Matta, R., Kim, H., Daly, B. (2022). Toward Supporting the Mental Health of Underprivileged Youth Through Village-Driven Sociotechnical Systems. Other. Proceedings of 20th European Conference on Computer-Supported Cooperative Work. European Society for Socially Embedded Technologies (EUSSET).
McCall, T., Kim, H., Lee, E., Lakdawala, A. M., Bolton, C. (2021). Content and Social Network Analyses of Depression-related Tweets of African American College Students. Other. 2597. Proceedings of the 54th Hawaii International Conference on System Sciences.
Kim, H., Laing, O. S., Yang, C. C. (2020). Detecting Potential Adverse Drug Reactions of Preschool ADHD Treatment Using Health Consumer-Generated Content. Other. Proceedings of 2020 IEEE International Conference on Healthcare Informatics (ICHI), IEEE.
Ortiz, M. S., Kim, H., Wang, M., Seki, K., Mostafa, J. (2019). Dynamic cluster-based retrieval and discovery for biomedical literature. Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 390--396.
Kim, H., Arguello, J. (2017). Evaluation of features to predict the usefulness of online reviews. Proceedings of the Association for Information Science and Technology. 54(1), 213--221. Wiley Online Library.
Kim, H., Bian, J., Mostafa, J., Jonnalagadda, S., Del Fiol, G. (2016). Feasibility of extracting key elements from ClinicalTrials. gov to support clinicians’ patient care decisions. AMIA Annual Symposium Proceedings. 2016, 705.
St\"ober, Jakob,, Heale, B. S., Fulghum, K., Del Fiol, G., Kim, H., Raja, K., Jonnalagadda, S. (2015). Concept based Information Retrieval for Clinical Case Summaries.. TREC.
Kim, J., Diesner, J., Kim, H., Aleyasen, A., Kim, H. (2014). Why name ambiguity resolution matters for scholarly big data research. 2014 IEEE International Conference on Big Data (Big Data). 1--6.
Journal Article
Esener, Y., McCall, T., Lakdawala, A., Kim, H. (2023). Seeking and Providing Social Support on Twitter for Trauma and Distress During the COVID-19 Pandemic: Content and Sentiment Analysis. Journal of Medical Internet Research. 25, e46343.
Kim, H., Oh, S. (2023). Everyday life information seeking in South Korea during the COVID-19 pandemic: daily topics of information needs in social Q&A. Online Information Review. 47(2), 414-430. Emerald Publishing Limited.
Choi, B., Kim, H., Huh-Yoo, J. (2021). Seeking Mental Health Support Among College Students in Video-Based Social Media: Content and Statistical Analysis of YouTube Videos. Other. 5(11), e31944. Toronto, ON: JMIR Publications. https://formative.jmir.org/2021/11/e31944/
Stevenson, R. D., Suomela, T., Kim, H., He, Y. (2021). Seven Primary Data Types in Citizen Science Determine Data Quality Requirements and Methods. Other. 3, 49. Frontiers.
Monselise, M., Greenberg, J., Liang, O. S., Pascua, S., Kim, H., Kelly, M., Boone, J. P., Yang, C. C. (2021). An Automatic Approach to Extending the Consumer Health Vocabulary. Other. 6(1), 35 - 49. Sciendo.
Slager, S. L., Weir, C. R., Kim, H., Mostafa, J., Del Fiol, G. (2017). Physicians’ perception of alternative displays of clinical research evidence for clinical decision support--a study with case vignettes. Other. 71, S53--S59. Elsevier.
Kim, J., Kim, H., Diesner, J. (2014). The impact of name ambiguity on properties of coauthorship networks.
Poster
Jung, ., Choi, B., Kim, H., Song, L. Care Need Assessment among Patients with Bladder Cancer and Their Family Members Using Online Forum. Atlanta:.
Kim, H., Choi, B. (2018). A comparative examination of factors that affect the credibility of health information on social media. Proceedings of the Association for Information Science and Technology. 55(1), 843--844. Wiley Online Library.
Slager, S., Weir, C. R., Kim, H., Mostafa, J., Del Fiol, G. (2015). Alternative Information Display of Clinical Research to Support Clinical Decision Making: A Formative Evaluation..

Awarded Grants

Contracts, Grants and Sponsored Research

Grant - Research
Kim, H. (Principal), "Automatic Named-Entity Recognition Annotation Tool for Drug Safety," Sponsored by College of Information, University of North Texas, $3000 Funded. (February 1, 2022January 31, 2023).
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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