Jinseok Kim

Photo of Jinseok Kimjkim362 [at] illinois.edu

Teaching this Semester

Advisor: 

Areas of Research: 

My main research topic encompasses how data processing can affect knowledge discovery from data. Data processing, as a preliminary step to data analysis, refers to validation, aggregation, cleaning, classification, disambiguation, and feature generation. In my dissertation, I argue that the choice of processing methods or assumptions can represent the same data in different ways, possibly leading to false positive or false negative findings and consequently to flawed decision making. Such impact may be amplified as data size increases, posing a risk to big data analysis. This problem has not been adequately addressed by information scientists. Thus, my research goal is to (1) measure the impact of data processing on our understanding of data under various conditions, such as data size, (2) identify error generation and propagation mechanism caused by data processing decisions, and (3) propose solutions to mitigate distortive effects of data processing on knowledge.

Field Exam Area: 

Socio-Technical Data Analytics (Data Science)

Dissertation: 

The impact of author name disambiguation on knowledge discovery from big scholarly data

Director of Dissertation Research: 

Jana Diesner

Education: 

University of Illinois at Urbana-Champaign (MA in communication, 2012)

Yonsei University (BA in English Literature, 2001)

Courses Taught: 

LIS 590SML: Social Media Analytics

Selected Publications, Papers and Presentations: 

Kim, J., Tao, L., Lee, S-K., & Diesner, J. (2016). Evolution and Structure of Scientific Co-publishing Network in Korea between 1948 - 2011. Scientometrics. DOI: 10.1007/s11192-016-1878-5

Kim, J., & Diesner, J. (2016). Distortive Effects of Initial-Based Disambiguation on Measurements of Large-Scale Coauthorship Networks. Journal of the Association for Information Science and Technology. DOI: 10.1002/asi.23489

Kim, J., & Diesner, J. (2015). Coauthorship Networks: A Directed Network Approach Considering the Order and Number of Coauthors. Journal of the Association for Information Science and Technology. DOI: 10.1002/asi.23361

Kim, J., & Kim, J. (2015). Rethinking the Comparison of Coauthorship Credit Allocation Schemes. Journal of Informetrics 9(3): 667-673. DOI: 10.1016/j.joi.2015.07.005

Kim, J., & Diesner, J. (2015). The Effect of Data Pre-processing on Understanding the Evolution of Collaboration Networks. Journal of Informetrics, 9(1), 226-236. DOI: 10.1016/j.joi.2015.01.002

Kim, J., & Diesner, J. (2014). A Network-Based Approach to Coauthorship Credit Allocation. Scientometrics. 101(1), 587-602. DOI: 10.1007/s11192-014-1253-3

Diesner, J., Kim, J., & Pak, S. (2014). Computational Impact Assessment of Social Justice Documentaries. Journal of Electronic Publishing. 17(3): A special issue on 'Metrics for Measuring Publishing Value: Alternative and Otherwise.' DOI: 10.3998/3336451.0017.306

Related Topics

Data Analytics, Informetrics, Science Processes, Social and Information Networks, Social Informatics, Social Media