1 - Introduction and Orientation

January 10/12

Wednesday: What is Computational Social Science?

Friday: Hello World: Let’s Code!

No lab due this week, but please make sure you have R and RStudio installed (you can follow instructions in the lab video). Check out Lab #0 (linked below) to make sure you got everything set up correctly.

Can Data Science Help us Fight COVID-19?


Required Reading:

Optional Materials:


Lab #0: Getting Started with RStudio

Lab #0: Example Lab Markdown File (Ungraded)

Lab Video Lecture: Getting Started with RStudio Ungraded Assignment: Install R and R Studio
Optional Resource:

2 - Ethics

January 17/19

Wednesday: How Can We Protect Human Subjects? class online

Friday: Coding Basics

Following Wednesday (1/24): Lab due by 5PM

Discussion questions for this week:

  1. Should researchers always be required to get the consent of the people they study?
  2. Is there ever a point where the scientific value of research should trump ethical concerns?
  3. Are the old ethical guidelines that Matt Salganik discusses in his book “Bit by bit” sufficient, or do we need new ones for the post-COVID era?

Ethics in Computational Social Science

Required Reading:

Optional Materials:

Lab #1: R Basics

Lab #1: R Basics (due the following Wednesday by 5PM)

Lab Video Lecture: R Basics
Materials from Video:
Optional Resource:

3 - Social Media & Polarization

January 24/26

Wednesday: Does Social Media Cause Harm?

Friday: Learning to Work with Data.

Discussion questions for this week:

  1. Have you ever been surprised by political developments that you did not see coming on social media?
  2. Do you feel like you are in an echo chamber on some platforms more than others? Why?
  3. Do you think TikTok makes the echo chamber effect stronger or weaker?

Following Wednesday: Lab due by 5PM

Do our Platforms Push us Apart?

Required Reading:

Optional Materials:

Lab #2: Data Wrangling

Lab #2: Data Wrangling (due the following Wednesday by 5PM)

Lab Video Lecture: Data "Wrangling"
Materials from Video:
Optional resources:

4 - The Echo Chamber

Jan 31/Feb 2

Wednesday: The Echo Chamber

Friday: Visualizing Society

Following Wednesday: Lab due by 5PM

Group discussion questions for this week:

  1. A major limitation of the study we read this week is that it only examined Twitter users– do you think exposing people to opposing views on Facebook, Instagram, TikTok or other platforms would have a similar effect? Why or Why not?
  2. The study found that Republicans tend to double-down in their pre-existing views when they are exposed to opposing views more strongly than Democrats - develop some hypotheses about why this might have happened;
  3. The accounts retweeted by the bots in the study retweeted high profile “opinion leaders” (e.g. elected officials, journalists, etc). Do you think the effects would have been different if they had retweeted non-elite partisans instead?

Should we Break our Echo Chambers?

Required reading:

Optional Materials:

Lab #3: Data Visualization

Lab #3: Data Visualization (due the following Wednesday by 5PM)

Lab Video Lecture: Data Visualization
Materials from Video:
Required resources:
Optional resources:

5 - Social Networks and Health

February 7/9

Wednesday: How do our Social Relationships Shape our Health?

Friday: Learning how to Iterate, Lecture by Noah Gibson

Following Wednesday: Lab due by 5PM

Group discussion questions for this week:

  1. How do the three causes of similarity—induction, homophily, and confounding—outlined by Nicholas Christakis, contribute to our understanding of patterns in voting behavior and political polarization?
  2. Reflecting on your personal experiences, in what ways has your social network influenced your health?
  3. What are some ways in which the structure of social networks has changed since the rise of social media?

The Hidden Influence of Social Networks

Required reading:

Optional Materials:

Lab #4: Programming Basics

Lab #4: Programming Basics (due the following Wednesday by 5PM)

Lab Video Lecture: Programming
Materials from Video:
Optional Resources:

6 - Getting a Job

February 14/16

Wednesday: How will Networks Shape your Career?

Group discussion questions for this week:

  1. How did you find your last job?
  2. do you know anyone who’s gotten their job from a weak tie?
  3. How could social network analysis help you find a job?

Friday: Let’s code up some networks!

Following Wednesday: Lab due by 5PM

How to find a job (and Succeed Once you Get One)

Required reading:

Optional Materials:

Lab #5: Coding Social Networks

Lab #5: Coding Social Networks (due the following Wednesday by 5PM)

Lab Video Lecture: Coding Social Networks
Materials from Video:
Required reading:
Optional resources:

7 - Surveillance and Privacy

February 21/23

Wednesday: Are you worried about who can see your data?

Friday: Collecting data from online sources

Following Wednesday: no lab due.

Surveillance Capitalism (Shoshana Zuboff)

Optional reading:

Optional Materials:

Lab #6: Working with APIs

Lab #6: Working with APIs (No lab this week!)

Lab Video Lecture: Working with APIs
Optional reading:
  • R wrapper for the Spotify API
  • R wrapper for the Twitter API,
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    8 - Algorithms and Discrimination

    February 28/March 1

    Wednesday: AI Bias

    Friday: Intro to text analysis

    Following Wednesday: Lab due by 5PM

    Group discussion questions for this week:

    1. Have you, personally, ever experienced an algorithm recommend something to you that you think might create social inequality? If so, tell the rest of your group about it.
    2. In this class, we always encourage you to evaluate issues with evidence or data. Can you think of a way to design a study that could measure whether algorithms create social inequality?
    3. Google, Facebook, and many other large companies have created large teams specifically dedicated to creating fairness in Artificial Intelligence. Do you think it’s possible for people on those teams to independently audit or evaluate social inequality without some type of bias?

    Challenging the Algorithms of Oppression (Safiya Noble)

    Required reading:

    Optional Materials:

    Lab #7: Introduction to text analysis

    Lab #7: Introduction to Text Analysis (due the following Wednesday by 5PM)

    Lab Video Lecture: Introduction to text analysis

    Materials from Video:
    Required reading:
    Optional resources:

    9 - Hate Speech & Radicalization

    March 6/8

    Wednesday: Does Hate Spread more Quickly Online?

    Friday: More Text Analysis!

    Following Wednesday: Lab due by 5PM

    Using Google Search to Track Radicalization

    Required reading:

    Optional Materials:

    Lab #8: Word counts and dictionaries

    Lab #8: Word Counts and Dictionaries (due the following Wednesday by 5PM)

    Lab Video Lecture: Word counts and dictionaries
    Materials from Video:
    Required reading:
    Optional resources:

    10 - Misinformation and Trolling

    March 20/22

    *Wednesday: Does Misinformation Work?

    *Friday: Word Embeddings; See class tutorial here

    Following Wednesday: Lab due by 5PM

    Discussion questions for this week:

    1. Have you ever been the target of trolling or a misinformation campaign? Was it successful? Why or why not?
    2. Do you think we need new studies to examine the role of misinformation about COVID that may be different than the type propagated by the Russia-linked IRA?
    3. What types of policies do you think that social media companies and the government should consider to address misinformation, if any?

    Did Russia’s Social Media Campaign Succeed?

    Required reading:

    Optional Materials:

    Lab #9: Final Project Hypotheses

    Lab #9: Final Project Hypotheses (due the following Wednesday by 5PM)

    11 - Protest and Censorship

    March 27/29

    Wednesday: Is Social Media Protest Different?

    Friday: Let’s build a Large Language Model Together!

    Following Wednesday: Lab due by 5PM

    Online Social Change (Zeynep Tufekci)

    Required Reading

    Gary King et al. 2014. Reverse Engineering Censorship in China.

    Optional Materials:

    Lab #10: Large Language Models

    12 - Modeling and Communication

    April 3/5

    Wednesday: Making a social science Model

    Friday: Communicating your results

    No More Labs– wor on your final projects instead

    Social Science Modeling (A Brief Introduction)

    Lab #7: Introduction to modeling (due the following Wednesday by 5PM)

    Lab Video Lecture: Modeling
    Materials from Video:
    Required reading:

    Communicating Your Research

    No graded assignment this week-- apply communication or collaboration skills to your final project instead.

    13 - Tutorials: 1-1 meetings

    April 10/12

    No class. Work on final presentation and paper.

    14 - Tutorials: 1-1 meetings

    April 17/19

    No class. Work on final presentation and paper.

    15 - Final Presentations

    April 24

    Final presentations during normal class time and place (11:45-1pm) . Will post sign-up sheet.

    16 - Final Paper Due

    Final paper DUE Thursday May 2nd at 5pm (submit via Slack DM to Professor Bail and TA).