Publications

Peer-reviewed Journal Publications

  1. Lim, Sunghoon, Conrad S. Tucker, Kathryn Jablokow, and Bart Pursel. “A semantic network model for measuring engagement and performance in online learning platforms.” Computer Applications in Engineering Education (https://doi.org/10.1002/cae.22033).
  2. Tuarob, Suppawong, Sunghoon Lim, and Conrad S. Tucker. “Automated Discovery of Product Feature Inferences within Large Scale Implicit Social Media Data.”  Journal of Computing and Information Science in Engineering 18, no. 2 (2018): 021017.
  3. Lim, Sunghoon, and Conrad S. Tucker. “Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers.” Journal of Mechanical Design 139, no. 11 (2017): 111409.
  4. Lim, Sunghoon, Conrad S. Tucker, and Soundar Kumara. “An unsupervised machine learning model for discovering latent infectious diseases using social media data.” Journal of Biomedical Informatics 66 (2017): 82-94.  (PubMed) – Please see “Penn State CHOT Real Time Social Network Map” (i) below and see his presentation at the IE 590: Industrial Engineering Colloquium.
  5. Lim, Sunghoon, and Conrad S. Tucker. “A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data.” Journal of Mechanical Design 138, no. 6 (2016): 061403. – Please watch his YouTube video (ii) below.

Peer-reviewed Journal Publications Under Review

  1. Lim, Sunghoon, and Conrad S. Tucker. “Mining Twitter Data for Causal Links between Tweets and Real World Outcomes.” Decision Support Systems (Under Review).

Peer-reviewed Conference Proceedings

  1. Lim, Sunghoon, Conrad S. Tucker, Kathryn Jablokow, and Bart Pursel. “Quantifying the Mismatch between Course Content and Students’ Dialogue in Online Learning Environments.” In ASME 2017 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, American Society of Mechanical Engineers, 2017. (DEC Technical Committee Best Paper Award consisting of a prize of $1,000) [Presentation] – Please watch his YouTube video (iii) below.

 

(i) Penn State CHOT Real Time Social Network Map

  • Created by Chonghan LeeVishnupriya Bakthisaran (his undergraduate student mentees), and himself based on the machine learning method in his journal article (Lim, Tucker, and Kumara, 2017) and funded by the Penn State CHOT.
  • Visualizes tweets containing population-health-related information in real-time.
  • Visualizes information from population health to personalized medicine through the different scope of the map interface.
  • Resets the data every 5,000 tweets.
  • clustered: provides the clustered view of the tweets from different geographical locations and can zoom-in to see the individual plots.
  • density: indicates concentrated geographical locations with multiple data.
  • Filter by term: allows the users to see filtered tweets containing a specific keyword.

(ii) YouTube video that introduces his journal article (Lim and Tucker, 2016)

(iii) YouTube video that introduces his conference proceeding (Lim, Tucker, Jablokow, and Pursel, 2017)