Schedule

1 - Introduction and Orientation

August 29-31

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

No Lab homework due this week - 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:

• 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 #0: Getting Started with RStudio

2 - Ethics

September 5-7

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

Following Monday: 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:

• 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 #1: R Basics

3 - Social Media & Polarization

September 12-14

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

Following Monday: Lab due by 5PM

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 #2: Data Wrangling

4 - The Echo Chamber

September 19-21

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

Following Monday: 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:

• 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 #3: Data Visualization

5 - Social Networks and Health

September 26-28

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

Following Monday: Lab due by 5PM

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 #4: Programming Basics

6 - Getting a Job

October 3-5

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

NOTE: due to spring break, the lab due date has been moved back.

Following Wednesday (Wednesday, October 12 by 5PM): Lab due by 5PM

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?

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 #5: Coding Social Networks

7 - Surveillance and Privacy

October 12

Wednesday: regularly scheduled class @ 1:45-3pm.

Following Monday: no lab due.

Surveillance Capitalism (Shoshana Zuboff)

Optional reading:

• Kieran Healy. Using Metadata to Find Paul Revere.

Optional Materials:

• Shoshanna Zuboff. You are Now Remotely Controlled

8 - Algorithms and Discrimination

October 17-19

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

Following Monday: 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:

• 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

Lab #6: Working with APIs

9 - Hate Speech & Radicalization

October 24-26

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

Following Monday: Lab due by 5PM

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 #7: Modeling (A Brief Introduction)

10 - Misinformation and Trolling

October 31 - November 2

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

Following Monday: 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:

• 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 #8: Introduction to text analysis

11 - Protest and Censorship

November 7-9

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

Following Monday: Lab due by 5PM

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 #9: Word counts and dictionaries

12 - Modeling and Communication

November 14-16

Monday and Wednesday: regularly scheduled class @ 1:45-3pm.

Following Monday: Lab due by 5PM

Lab #10: Topic Modeling

Lab: Communicating Your Research

13 - Tutorials: 1-1 meetings

November 21

No class. Work on final presentation and paper.

14 - Tutorials: 1-1 meetings

November 28-30

No class. Work on final presentation and paper.

15 - Final Presentations

December 5-7

Final presentations during normal class time. Will post sign-up sheet.

16 - Final Paper Due

Final paper DUE December 16 by 5pm.