Lingshu Hu Assistant Professor of Business Administration

Lingshu Hu

Huntley 109
Curriculum Vitae

Professor Hu joined the faculty of the Williams School in September 2021. With a PhD in computational journalism and an MS in computer science, his primary teaching interests include making data analytics accessible to students in the social sciences and helping students master storytelling skills with data analytics and visualization.

Professor Hu’s research focuses on developing computational methods—such as machine learning, deep learning, and natural language processing—to examine patterns and effects of communication in computer-mediated environments. Professor Hu has collaborated with scholars from and applied computational methods in various areas including marketing, management, public relations, social psychology, and political communication. He is also an R and Python programmer and develops software to facilitate social science research.


Ph.D. in Journalism (Computational Methods), University of Missouri, School of Journalism (2021)
M.S. in Computer Science, University of Missouri, Department of Electrical Engineering & Computer Science (2022)
M.S. in Gender, Media, and Culture, London School of Economics and Political Science (2012)


  • Machine Learning, Deep Learning, and NLP
  • Communication Patterns and Effects
  • Social Media Analytics
  • Social Identities
  • Digital Public


BUS 202 - Fundamentals of Business Analytics

BUS 306A - Applied AI and Machine Learning

INTR 202 - Applied Statistics

Selected Publications

Hu, L. (2024). Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter. Online Information Review. 

Hu, L. (2023). A two-step method for classifying political partisanship using deep learning models. Social Science Computer Review. 

Xu, M., Hu, L., & Hinnant, A. (2023). Pseudo-events: Tracking mediatization with machine learning over 40 years. Computers in Human Behavior, 144, 107735.

Xu, M., Hu, L., & Cameron, G. T. (2023). Tracking moral divergence with DDR in presidential debates over 60 Years. The Journal of Computational Social Science, 6, 339–357.

Hu, L., Li, C., Wang, W., Pang, Bin., & Shang, Y. (2022). Performance Evaluation of Text Augmentation Methods with BERT on Small-sized, Imbalanced Datasets. The Proceedings of the 2022 IEEE International Conference on Cognitive Machine Intelligence (CogMI).

Li, C., Wang, W., Balducci, B., Hu, L., Gordon, M., Marinova, D., and Shang, Y. (2022) Deep Formality: Sentence Formality Prediction with Deep Learning. The Proceedings of the 2022 IEEE International Conference on Information Reuse and Integration for Data Science.

Zhang, W., Hu, L., & Park, J. (2022). Politics go “viral”: A computational text analysis of crisis attribution regarding the COVID-19 pandemic. Social Science Computer Review. 

Hu, L., Kearney, M. W., & Frisby, C. M. (2021). Tweeting and retweeting: Gender discrepancies in discursive political engagement and influence on Twitter. Journal of Gender Studies. 

Hu, L. (2021). Self as brand and brand as self: A 2x2 dimension conceptual model of self-branding in the digital economy. Journal of Internet Commerce, 20(3), 355–370. 

Hu, L., & Kearney, M. W. (2021). Gendered tweets: Computational text analysis of gender differences in political discussions on Twitter. Journal of Language and Social Psychology, 40(4), 482–503.