Welcome!

We live in an era where many aspects of our social interactions are recorded as textual data, from social media posts to medical and financial records. This course is about using a variety of techniques from machine learning and theories from social science to study human behaviors and important societal questions at scale. Topics will include methods for natural language processing and causal inference, and their applications to important societal questions around hate speech, misinformation, and social movements.

Schedule

Note: tentative schedule is subject to change.

Week Date Theme Course Material
1 April 2
Tuesday
Class Introduction
[slides]
Optional Readings:
1 Apr 4
Thursday
Computational basics and Hypotheses Testing
[slides]
Optional Reading:
  • Dan Jurafsky and James H. Martin. Speech & language processing. Chapter 5. 2021.
2 Apr 9
Tuesday
Working with Text Data / Project Introduction
[slides]
Optional Reading:
  • Dan Jurafsky and James H. Martin. Speech & language processing. Chapter 2. 2021.
💻 Homework 1
2 Apr 11
Thursday
Inferring Sentiment and Affect
[slides] [Project Overview slides]
Optional Reading:
  • Dan Jurafsky and James H. Martin. Speech & language processing. Chapter 25. 2021.
💻 Homework 1 [Colab Notebook]
3 Apr 16
Tuesday
Social Influence
[Group Presentation slides]
Required Reading: Optional Reading:
3 Apr 18
Thursday
Framing and Persuasion
[Group Presentation slides]
Required Reading: Optional Reading:
4 Apr 23
Tuesday
Topic Modeling for the Social Sciences
[slides]
Optional Reading:
  • Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent dirichlet allocation." Journal of machine Learning research 3, no. Jan (2003): 993-1022.
  • Ramage, Daniel, Evan Rosen, Jason Chuang, Christopher D. Manning, and Daniel A. McFarland. "Topic modeling for the social sciences." In NIPS 2009 workshop on applications for topic models: text and beyond, vol. 5, no. 27, pp. 1-4. 2009.
💻 Homework 2 [Colab Notebook]
4 Apr 25
Thursday
Network and Social Media
[Group Presentation slides]
Required Reading: 🗓Project proposal due
5 Apr 30
Tuesday
Word Embeddings and Representation
[slides]
Optional Reading:
5 May 2
Thursday
🔥 Project Pitch Session 📂Project proposal grade out
6 May 7
Tuesday
Group Presentation + Guest Lecture: Dr. Kristina Gligoric
[Group Presentation slides]
Required Reading: Optional Reading: 💻 Homework 3
6 May 9
Thursday
Fake News and Misinformation (1)
[Group Presentation slides]
Required Reading: Optional Reading:
7 May 14
Tuesday
Deep Learning for CSS
[slides]
🗓Project midway report due
7 May 16
Thursday
Fake News and Misinformation (2)
[Group Presentation slides]
Required Reading:
8 May 21
Tuesday
Open Questions in CSS
[slides], Prejudice and Stigma
[Group Presentation slides]
Required Reading: Optional Reading: 💻 Homework 4
8 May 23
Thursday
Causal Inference
[slides]
Optional Reading: 05/24: Course withdrawal deadline 📂 05/23: Project midway grade out
9 May 28
Tuesday
Prosocial Behavior
[Group Presentation slides]
Required Reading: Optional Reading:
9 May 30
Thursday
LLMs and Social Behaviors
[Group Presentation slides]
Required Reading: Optional Reading:
10 Jun 4
Tuesday
No Class. Work on Final Projects
10 Jun 6
Thursday
💥Poster Sessions 06/05: Last day of class
Jun 7
Friday
🗓Final report due

Overview

Course Info

Office Hours

Prerequisites

Academic Accommodations

Well-Being, Stress Management, & Mental Health