Schedule

1 - Orientation

January 21

Please attend an the entire class zoom session from 12:30-1 (link available in our Slack workspace).

2 - Introduction

January 24-30

Please attend your one-on-one meeting with Dr. Bail at your regularly scheduled time.

Can Data Science Help us Fight COVID-19?

Required Reading:

• Matthew Salganik. (1) Bit by Bit: Introduction, (2) Bit by Bit: Observing Behavior

Optional Materials:

• David Lazer et al. Life in the network: the coming age of computational social science.
• David Donoho. 50 Years of Data Science
• Eszter Hargittai. Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites.
• Joshua Blumenstock et al. Predicting Poverty and Wealth from Mobile Phone Data.
• Matthew Salganik. Bit by Bit: Asking Questions.

Lab: Getting Started with RStudio

3 - Ethics

January 31-February 6

Please attend the small group discussion section on 12:30-1:30pm Tuesday.

We will discuss the following questions:

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:

• Matthew Salganik. Bit by Bit: Ethics.

Optional Materials:

• Adam Kramer, Jamie Guillory, & Jeffrey Hancock. Emotional Contagion.
• Robinson Meyer. Everything We Know About Facebook’s Secret Mood Manipulation Experiment.
• Matt Salganik. Video: Ethics.

Lab: R Basics

4 - Social Media & Polarization

February 7-February 13

Please attend your one-on-one meeting with Dr. Bail at your regularly scheduled time.

Do our Platforms Push us Apart?

Required Reading:

• John Bohannon. Is Facebook keeping you in a political bubble?.
• Eytan Bakshy et al. Exposure to ideologically diverse news and opinion on Facebook.

Optional Materials:

• Eli Pariser Beware Online Filter Bubbles
• Pablo Barbera & Zachary C. Steinert-Threlkeld How to Use Social Media Data for Political Science Research.

Lab: Data Wrangling

5 - The Echo Chamber

February 14-20

Please attend your small group Zoom meeting from 12:30-1:30pm Tuesday.

We will discuss the following questions:

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:

• Chris Bail, et al. Exposure to opposing views on social media can increase political polarization.

Optional Materials:

• Qi Yang et al. Mitigating the Backfire Effect.
• Chris Bail. Video: Building Apps and Bots for Social Science Research.

Lab: Visualization

6 - Social Networks and Health

February 21-27

Please attend your one-on-one meeting with Dr. Bail at your regularly scheduled time.

The Hidden Influence of Social Networks

Required reading:

• Nicholas Christakis & James Fowler. Connected: The Surprising Power of Our Social Networks and How they Shape Our Lives (Chapter One).

Optional Materials:

• Duncan Watts. How small is the world, really?.
• David Austin. How Google Finds Your Needle in the Web’s Haystack.

Lab: Programming Basics

7 - Getting a Job

February 28-March 6

Please attend your small group Zoom meeting from 12:30-1:30pm Tuesday.

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

Required reading:

• Charles Kadushin. Making Connections: An Introduction to social network concepts and findings (Intro and Chapter One)

Optional Materials:

• Mark Granovetter. The Strength of Weak Ties.
• Carolyn Bentley. Introduction to Structural Holes Theory.

Lab: Coding Social Networks

8 - Surveillance and Privacy

March 7-13

One-on-one meetings cancelled this week for “spring break.” If you just can’t get enough computational social science, check out this video documentary!

Surveillance Capitalism (Shoshana Zuboff)

Optional reading:

• Kieran Healy. Using Metadata to Find Paul Revere.

Optional Materials:

• Shoshanna Zuboff. You are Now Remotely Controlled

Lab: Working with APIs

9 - Algorithms and Discrimination

March 14-20

Please attend your small group Zoom meeting from 12:30-1:30pm Tuesday.

We will discuss the following questions:

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:

• Sendhil Mullainathan. Biased Algorithms Are Easier to Fix Than Biased People.
• Alisha Haridasani Gupta. Are Algorithms Sexist?.
• Gavin Edwards. Machine Learning, An Introduction.

Optional Materials:

• Karen Hao. How Facebook got addicted to spreading misinformation
• Alex Hanna and Meredith Whittaker. Timnit Gebru’s Exit From Google Exposes a Crisis in AI
• David Lazer et al. The Parable of the Google Flu

Modeling (A Brief Introduction)

10 - Hate Speech & Radicalization

March 21-27

Please attend your one-on-one meeting with Dr. Bail at your regularly scheduled time.

Using Google Search to Track Radicalization

Required reading:

• Alexandra Siegal & Vivienne Badaan. #No2Sectarianism: Experimental Approaches to Reducing Sectarian Hate Speech Online.

Optional Materials:

• Paris Martineau. Maybe It’s Not YouTube’s Algorithm That Radicalizes People
• Kevin Munger. Tweetment Effects on the Tweeted
• Chris Bail et al. Using Internet Search Data to examine the relationship between anti-Muslim and pro-ISIS sentiment in U.S. counties

Lab: Intro to text data

11 - Misinformation and Trolling

March 28-April 3

Please attend your small group Zoom meeting from 12:30-1:30pm Tuesday.

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:

• Gordon Pennycook and David Rand, Fighting misinformation on social media using crowdsourced judgments of news source quality

Optional Materials:

• Andrew Guess et al. Less than you think: Prevalence and predictors of fake news dissemination on Facebook.
• Chris Bail et al. Asessing the Impact of the Russian Internet Research Agency’s Impact on the Political Attitudes and Behaviors of U.S. Twitter Users.
• The Supreme Court of Faceboo‪k‬ on The New Yorker Radio Hour

Lab: Word counts and Dictionaries

12 - Protest and Censorship

April 4- April 10

Please attend your one-on-one meeting with Dr. Bail at your regularly scheduled time (some meetings this week may need to be rescheduled).

Online Social Change (Zeynep Tufekci)

Required Reading

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

Optional Materials:

• Julia Silge and David Robinson (2020). Sentiment analysis with tidy data in Text Mining with R.

Lab: Your First Topic Model

13 - Wellness Week

April 11-17

No Lectures/Lab or meetings… work on final projects!

14 - Open Lab

April 18-24

(Discuss your final project or any other topic you wish during one-on-one meetings with Dr. Bail at your regularly scheduled time)

Regularly scheduled TA office hours will also be available.

Required reading:

• David Holtz et al. Interdependence and the cost of uncoordinated responses to COVID-19

Optional Materials:

Lab: Communicating Your Research

15 - Final Presentations

April 25-May 1

Final presentation times:

• Monday, April 26 @ 1-3pm
• Tuesday, April 27 @ 1-3pm

Final paper DUE Saturday May 1st at 5pm EDT