Li Zeng
  • Prof. Cobbenhagenlaan 125, Reitse Poort, room RP 006A

    5037 DB Tilburg


Accepting PhD Students


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Personal profile


Dr. Li Zeng is an Assistant Professor at the Department of Methodology and Statistics at Tilburg University. Dr. Zeng develops theory and methods for the analysis of large-scale social media data with a focus on how such data can be used to better understand human behavior and improve social systems, combining techniques from Machine Learning, Social Network Analysis, and Natural Language Processing. Her research has been published in AAAI ICWSM, IEEE HICSS, iConference and Field Methods.

Dr. Zeng earned her Ph.D. in Information Science from the University of Washington. She also holds a M.S. in Information Science from the University of Washington and a B.A. in Information Management and Information Systems from the Communication University of China. She was a Pre-Doctoral Lecturer at UW iSchool, teaching undergraduate-level research methods, data science courses, and graduate-level social media data mining. She has received several fellowships and awards including William and Ruth Gerberding Endowed Fellowship, the Chinese National Scholarship, China Central Television Scholarship, Tokyo Broadcasting System Television Inc. Scholarship, University of Washington Top Scholar Award, a Best Paper Award by the International Conference on Social Computing and Social Media, and a Lee Dirks Best Paper Award Finalist at the iConference.

Research interests

Dr. Zeng develops theories and methods of the analysis of large-scale social data to better understand human behavior and improve social systems. Lying in the emerging field of computational social science, her work is rooted in social science theories and utilizes digital traces to test domain theories at scale providing novel insights. Her projects incorporate expertise in machine learning, social network analysis, and natural language processing. Part of her work studies how high-volume, dynamic, crisis-related informal online communication. This work sheds light on rumoring behavior during crisis events, as well as how the collective intelligence of the crowd can be utilized to distinguish misinformation and aid in its detection. Dr. Zeng's doctoral dissertation examines social interaction in online fitness communities, with a focus on how social interaction and peer support are linked to fitness behavior and behavior change. This work enables novel insights about health promotion through network processes. 


2021/22 Block 3 - Data Mining (JBI030) *jointly teaching with TU/e

2021/22 Block 4 - Introduction to Data Science (500189-B-6)

Current courses

Click here for my courses.


  • Data Science
  • Machine Learning
  • Computational Social Science
  • Social Network Analysis
  • Health Informatics
  • Crisis Informatics
  • Online Data Collection


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