Computational Social Science

MW 3:30-4:45pm, Hybrid


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.

Instructor



Piazza:
piazza.com/gatech/spring2021/cs6471
Online Office Hours (Eastern Time):

Schedule

Note: tentative schedule is subject to change.

Date Topic Readings
Jan 20 Introduction to CSS
Slides
Jan 25 Lecture: NLP and Machine Learning Basics
Slides
Jan 27 Bias and Fairness: Biases
Slides
Feb 1 Bias and Fairness: Debiasing
Slides
Feb 3 Introduction to Project Ideas
Slides
Feb 8 Language Civility: Hate Speech and Toxicity
Slides
Feb 10 Guest Lecture from David Muchlinski
Slides

Grading

  • 50% Project
    • Project Proposal (10%)
    • Midterm Report (15%)
    • Final Report (20%)
    • Project Presentation (5%)
  • 10% Quiz
  • 20% Reading Responses
  • 20% Presentation

Policies

Class Policies:

Attendance will not be taken, but you are responsible for knowing what happens in every class. The instructor will try to post slides and notes online, and to share announcements, but there are no guarantees. So if you cannot attend class, make sure you check up with someone who was there.

Prerequisites

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.

FAQs

  • 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.