Computational Social Science

MW 3:30-4:45pm, East Architecture 207

Course Information

This course is about using a variety of techniques from NLP, ML, and social science to develop a broad understanding of the emerging cross-disciplinary field of Computational Social Science. Example topics include methods of text analyses, and applications to social science fields, such as political science, sociolinguistics, sociology, and economics.

Certain slides, and materials for this course are borrowed from Jacob Eisenstein, David Bamman at UC Berkeley, and Robert Kraut at CMU.


Office Hours (Eastern Time):
Caleb Ziems:
Mondays 4:45PM - 5:45PM (Right After Class)
Location: West Architecture Classroom 260 (and BlueJeans)

Shang-Ling Hsu
Thursdays 11AM-12PM
Location: College of Computing Building, First Floor Commons (and BlueJeans)

Prof. Diyi Yang
By Appointment


Note: tentative schedule is subject to change.

[🚀] indicates optional reading.

Date Topic Readings
Jan 10 Introduction to CSS
Jan 12 Lecture: NLP and Machine Learning Basics
Jan 17 No Class: MLK Day
Jan 19 Bias, Fairness, and Ethics 1
Jan 24 Bias, Fairness, and Ethics 2
Jan 26 Introduction to Project Ideas
Jan 31 Language Civility 1
Feb 2 Language Civility 2
Feb 7 Guest Lecture from Su Lin Blodgett on:
Language, Language Technologies, and Justice
(hover for abstract)
Feb 9 Lecture: Social Science Theories
Feb 14 Computational cognitive modeling 1
Feb 16 Computational cognitive modeling 2
Feb 21 Language Influence 1: Persuasion / Neogiation
Feb 23 Language Influence 2: Deception
  • Peskov, Denis, Benny Cheng, Ahmed Elgohary, Joe Barrow, Cristian Danescu-Niculescu-Mizil, and Jordan Boyd-Graber. "It Takes Two to Lie: One to Lie, and One to Listen." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3811-3854. 2020.
  • Pérez-Rosas, Verónica, Mohamed Abouelenien, Rada Mihalcea, and Mihai Burzo. "Deception detection using real-life trial data." In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 59-66. 2015.
Feb 28 Project Consultation (in-class)
March 2 Information Diffusion 1
March 7 Information Diffusion 2
March 9 Mid-way Project Presentations
March 14 Cross-Cultural CSS 1
March 16 Cross-Cultural CSS 2
March 21 No Class: Spring Break
March 23 No Class: Spring Break
March 28 Guest Lecture from Katherine Keith
Text and Causal Estimation: Text as Causal Confounders and Mediators
(hover for abstract)
March 30 Lecture: Statistics & Casual Inference Basics
April 4 Multimodal CSS 1
April 6 Multimodal CSS 2
April 11 Social Roles and Teamwork
April 13 Human AI Interaction
April 18 Social Movements
April 20 Final Project Presentation 1
Final Presentations
Apr 25 Final Project Presentation 2
Final Presentations


  • 50% Project
    • Project Proposal and Literature Review (10%)
    • Midterm Report (15%)
    • Final Report (20%)
    • Project Presentation (5%)
  • 25% Reading Responses
  • 15% In-Class Presentations
  • 5% Attendance Quiz
  • 5% In-Class Discussion


The course is designed for graduate students who are interested in natural language processing and computational social science. Prerequisites: a course in artificial intelligence or any relevant field (e.g., NLP, ML); proficiency with using ML/NLP tools.

Furthermore, this course assumes:

  • Good coding ability, corresponding to at least a third or fourth-year undergraduate CS major.
  • Background in basic probability, linear algebra, and calculus.
  • Familiarity with machine learning is helpful but not assumed. Of particular relevance are linear classifiers: naive Bayes, and logistic regression.


  • The class is full. Can I still get in?

    Sorry. The course admins in CoC control this process. Please talk to them.

  • I am graduating this Fall and I need this class to complete my degree requirements. What should I do?

    Talk to the advisor or graduate coordinator for your academic program. They are keeping track of your degree requirements and will work with you if you need a specific course.

  • I have a question. What is the best way to reach the course staff?

    Registered students – your first point of contact is Piazza (so that other students may benefit from your questions and our answers). If you have a personal matter, please email us.