Course Description & Policies

INSTRUCTOR

COURSE DESCRIPTION

The past decade has witnessed an explosion of data produced by websites such as Twitter, Facebook, Google, and Wikipedia, the mass digitization of administrative and historical records, and the rapid expansion of mobile technology into nearly every corner of our lives. A new wave of techniques for collecting, classifying, and analyzing these data hold enormous potential to address many of the most urgent questions in social science: How do diseases spread? What causes financial meltdowns? How did America become so politically polarized? This course surveys the inter-disciplinary field of computational social science, which combines insights from computer and information science, sociology and social network analysis, economics, political science, and public health in order to answer such questions.

COURSE GOALS

This course requires no prior knowledge of computer programming or social science. Students will learn to ask social science questions, and learn how to answer them by collecting data from digital sources such as social media sites. Students will also acquire skills in large language models, social network analysis, automated text analysis, application programming interfaces, and the R programming language.

OVERVIEW OF CLASS FORMAT

Each week, there will be two lectures that average 10-30 minutes each. One of these lectures will be about a social science topic (e.g. political polarization or public health), and the other is designed to introduce you to the programming you need to learn to complete the lab assignment for that week. There are also required readings for each week that build upon the content in the lectures, and help introduce you to the material you need to learn for the labs. The lab assignments do not always line up with the content of the social science lectures; instead, the goal is to introduce both social science and coding gradually/incrementally. In order to help you prepare for our substantive discussions about each social science topic, there are weekly “discussion questions” posted on the course website.

Required Readings
We will draw heavily on two excellent books, both of which are freely available online thanks to the generosity of the authors. If you can afford to purchase a hard copy to show your appreciation for their hard work, please consider it.

• Bit by Bit: Social Research in the Digital Age. Matthew J Salganik (2017), Princeton University Press
• R for Data Science. Garrett Grolemund & Hadley Wickham, O’Reilly: )
  

R & RStudio
In this class, we will use R, a free programming language that I will teach tou how to install during the first week of class. There are a variety of different ways to use R, but the most common way to do so is with the software RStudio, a free Graphical User Interface which you can either run on your laptop, or via a web server. Though it is possible to run it on a Tablet or Microsoft Surface, I recommend using a laptop or desktop if possible. I will discuss how to install and set up R during the first week of class.

Slack
We will be using Slack to communicate with each other. Slack is a messaging platform that let’s us share code, web links, and other things easily- it also allows me to make announcements to the class and share other things I think you might find interesting (e.g. internship announcements or articles that build upon our discussions with each other). Slack is also used by many different companies and organizations in and outside the tech world, so this might be a great chance to learn how to use it if you have not already. You can read more about getting started with Slack here.

The Slack channel invite will be sent to your email before the first day of class, but please contact the TA if you have not received it. I do not post the link to our Slack Workspace on this site in order to make sure access is restricted to people enrolled in the class at Duke.

ChatGPT/Large Language Model Policy

ChatGPT and other Large Language Models (LLMs) are permitted in this class for some, but not all assignments. LLMs are permitted for use in labs and in-class coding exercises. But please be aware that LLMs can sometimes create more problems than they solve. Research indicates LLMs can be useful for learning how to write software code. But they are most useful to people who are already familiar with core concepts in software development who know what terms to use to prompt models to produce the code they hope to create. If you are new to coding, I encourage you not to rely too heavily on LLMs for this reason. LLMS can be useful for interpreting code (i.e. explaining how it works), but remember that most LLMs remain prone to “hallucinations” that might inaccurately describe code, or produce code that does not run.

ChatGPT and other LLMs are not permitted for drafting text for your final paper. Though LLMs can easily write essays about well known topics, they cannot perform causal inference correctly, and therefore cannot write a research paper like the one you are assinged to create for this class. I do not employ any Generative AI detection tools in this class to review your writing, since research indicates they are not reliable. Instead, compliance with this policy relies upon your compliance with the Duke Community Standard.

Stack Overflow
The field of computational social science is growing so rapidly that none of the resources I give you will remain at the cutting edge for long. You will almost certainly encounter issues unique to the data we collect as part of our final research projects and/or incompatibilities between software packages and/or your computer. Stack Overflow is a website where computer programmers help each other solve such problems. Individuals ask questions, and others earn “reputation points” for solving their problems—these reputation points are awarded by the person who asks the question as well as other site users who vote upon the elegance/efficiency of each solution. For you, this reputation system means you can quickly identify the most high-quality solutions to your problems. Take a tour of the site here.

Lecture Videos

If you miss something from a lecture, I have recorded many of them in high quality 4k video and posted them on our website to help you review before labs and/or drafting your final paper.

FORMAL REQUIREMENTS

Weekly Readings
You are responsible for reading and understanding the assigned readings each week. Make use of your fellow students, your TA, the Internet, a dictionary, and me to ensure that you understand the readings. Using Large Language Models to summarize readings is not permitted.

This syllabus is a ‘living document.’ By this I mean I reserve the right to change the reading assignments in response to your feedback as well as my own sense of our group achievement. No changes will be made without at least two weeks of advance notice. Each week, we also provide you with a list of “optional readings” in case you want to go deeper into the material that is covered in the assigned readings.

Weekly Lab Assignments
By 5PM on the listed due date, you will be required to complete and submit the assigned lab exercises described on the course schedule on this website. You must submit your lab assignments in a format called “R markdown” (abbreviated .Rmd). A video that describes how to create files in this format is available on the Schedule for the second lab (the first lab assignment is ungraded).

Students are permitted to miss one lab assignment without penalty. Assignments will be graded as follows: 100% (Student writes code that successfully completes all tasks assigned); 90% (Student writes code that successfully completes all but one of the assigned tasks); 80% (Student writes code that successfully completes all but two of the assigned tasks); 70% student writes code that completes all but three of the assigned tasks); 0% (Student does not write code that completes assignments). Be sure to follow the guidelines posted in the Lab Instructions document at the top of each lab.

Please Submit all homework assignments to our TA (Noah Gibson) via direct message on Slack.

Final Project
Numerous studies arrive at the same conclusion: students learn more when they are actively engaged in activities in class (even if they sometimes think they learn less through such activities). The challenge, then, is for us all to think of how you can get more engaged, and in my experience the best way for you to do this is to try doing some research yourself. This may sound like a lofty goal, but my bet is that we will learn much more while failing to achieve an ambitious goal than if we do not try.

Your goal for your final project is to a) ask a research question relevant to one of the topics we cover in social science (e.g. misinformation, algorithms and discrimination, or public health); b) explain why this topic is important (to social science and/or the world); c) develop at least one hypothesis to answer this question; d) collect data that allows you to test this hypothesis; and e) describes whether or not your hypothesis was confirmed, and what implications this should have for people who want to do future research on your topic. Finally, f) You must also cite at least six scholarly sources to support or develop your hypotheses and/or interpret the conclusions of your research.

Whether you find support for your hypothesis will not effect your grade. Instead, you will be evaluated based upon a) the quality of the research question you ask and the hypotheses you develop; and, b) the quality of the data collection and analysis. If your analysis does not support your hypothesis– and your hypothesis is a well-founded one– then I consider this to be an important finding. In fact, I have published research that does not confirm my initial hypotheses as well (For example, see here).

If you have questions about your grade for the final project at any time we can discuss them during our one-on-one meetings- the earlier you begin thinking about and developing your project, the more quickly and efficiently I can give you feedback to help you achieve the grade you desire.

The final project will consist of three parts: two individual meetings with me (during our regular class time), a final presentation, and a final paper.

One-on-one Tutorial Meetings To help you develop your final project, I ask that you meet with me individual towards the end of the semester so that we can discuss how to best identify a research question and suitable methods to answer it. These meetings will occur during our regularly scheduling class time, and a sign-up link for the meetings will be posted on slack several weeks before the first set of one-on-one meetings. All one-on-one meetings will be held in Dr. Bail’s office, or via Zoom.

Your final project presentation will be an additional opportunity to get feedback from Prof. Bail and your fellow students that can help you write a better paper. The format of the presentation is entirely up to you. Feel free to use Google Slides, Powerpoint, or anything else that suits you. The presentations will be total 10 minutes including 8 minutes of presentation time and 2 minutes for questions and feedback. The presentation itself is ungraded, so only the final paper will be included in your final grade. Final presentations will happen during normal class hours - see course schedule for the sign-up link.

Your final project paper should be submitted as both an R Markdown (.Rmd) file and a knitted web page (.html) file by the deadline on Thursday, May 2nd at 5pm EDT. It should be at least 2,500 words and the knitted web page SHOULD NOT include code blocks. The written component grading will follow this rubric, and it should include the following components:

A) an introduction in which you ask the research question and explain why it is important; B) a section where you define key concepts in your study and present hypotheses to answer your research question; C) a detailed explanation of how you collected the data to test your hypotheses; D) a description of the analysis techniques you used to analyze your data; E) a detailed description of the results of your analysis with at least two figures, and an interpretation of what they mean for past and future research on your subject; F) a bibliography.

My goal is for you to produce something that you can show to future employers, graduate schools, or even just your friends and families to showcase what you have learned. You can see some previous final project papers at the bottom of this course description website (link). You can see examples of student projects from previous semesters of this course at the links below.

Attendance and participation includes attending all classes, completing the required readings before the beginning of each class, and actively participating in group and class discussions. We will take attendance in the form of an in-class questionnaire about the readings at the beginning of every class, so please bring an electronic device such as a phone, tablet, or computer so that you can complete it. You will be allowed to miss up to three classes before points will be deducted.

How Your Grade Will be Calculated

• Final Project Paper 60%
• Lab exercises 20%
• Attendance and Participation 20%
  

COURSE POLICIES

The Duke Compact recognizes our shared responsibility for our collective health and well-being. Please be reminded that by signing your name to this pledge, you have acknowledged that you understand the conditions for being on campus (if you are on campus this semester). These include complying with university, state, and local requirements and acting to protect yourself and those around you. For complete language and updated policies, please visit this link

Academic Integrity/the DCS

All students, whether residing on campus or learning remotely, must adhere to the Duke Community Standard (DCS): Duke University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, and accountability. Citizens of this community commit to reflect upon these principles in all academic and non-academic endeavors, and to protect and promote a culture of integrity. Plagiarism, cheating or other violations will be dealt with according to University policy. All student assignments will be processed by plagiarism detection software.

Mental Health and Wellness

We are living through unprecedented times that are creating tremendous challenges for everyone. If your mental health concerns and/or stressful events negatively affect your daily emotional state, academic performance, or ability to participate in your daily activities, many resources are available to you, including the ones listed below. I encourage all students to access these resources, particularly as we navigate the transition and emotions associated with this time.

DukeReach. Provides comprehensive outreach services to identify and support students in managing all aspects of wellbeing.

Counseling and Psychological Services (CAPS). CAPS services include individual, group, and couples counseling services, health coaching, psychiatric services, and workshops and discussions. They can be reached at (919) 660-1000

TimelyCare. An online platform that is a convenient, confidential, and free way for Duke students to receive 24/7 mental health support through TalkNow and scheduled counseling.

Duke offers several resources for students to both seek assistance on coursework and improve overall wellness, some of which are listed below and described in more detail at this link

• The Academic Resource Center: (919) 684-5917, theARC@duke.edu, or arc.duke.edu,
• DuWell: (919) 681-8421, duwell@studentaffairs.duke.edu, or https://studentaffairs.duke.edu/duwell)
• WellTrack: https://app.welltrack.com/

Accessibility

In addition to accessibility issues experienced during the typical academic year, I recognize that remote learning may present additional challenges. Students may be experiencing unreliable wi-fi, lack of access to quiet study spaces, varied time-zones, or additional responsibilities while studying at home. If you are experiencing these or other difficulties, please contact me to discuss possible accommodations.

Technology Accommodations Students with demonstrated high financial need who may have limited access to computers and stable internet may request assistance in the form of loaner laptops and WIFI hotspots. For new Spring 2024 technology assistance requests, please go here. Please note that supplies are limited.

Academic Accommodations The Student Disability Access Office (SDAO) will continue to be available to ensure that students are able to engage with their courses and related assignments. Students should be in touch with the Student Disability Access Office to request or update accommodations under these circumstances. Zoom has the ability to provide live closed captioning. If you are not seeing this, and but would like to see this feature, please reach out to Duke OIT for assistance.

Accommodations for Remote Students If you are unable to attend one of our group meetings, please contact me and we can discuss how to accommodate your needs during this very challenging time.

Religious Accommodations Students are permitted by university policy to be absent from class to observe a religious holiday. Accordingly, Trinity College of Arts & Sciences and the Pratt School of Engineering have established procedures to be followed by students for notifying their instructors of an absence necessitated by the observance of a religious holiday. Please submit requests for religious accommodations at the beginning of the semester so that we can work to make suitable arrangements well ahead of time. You can find the policy and relevant notification form here: https://trinity.duke.edu/undergraduate/academic-policies/religious-holidays.

Pronouns If you would like to specify the pronoun you prefer, please do so in DukeHub.

Office Hours
Please contact me in advance if you would like to visit my office hours.

FINAL PROJECT EXAMPLES

Fall 2022

Controversy on Twitter: How Certain Political Candidates Choose to Convey their Platforms to Constituents by Michelle Qiu

Spring 2022

Public Interest In Covid-19 by Sophie Sanda

Diversion in the Digital Age: How U.S. Politicians Portray Foreign Countries on Social Media During COVID-19 by Selina Chu

How We Forget Mass Shootings: An Analysis of Google Trends for Mass Shooting Events by Joe Choo

Birdwatch and the Polarization of the Crowds by Ryan Champaigne

Fall 2020

Hospice-related Google Search Volumes and Hospice Utilization by Mihir Patel

The Gender Gap in Professional Networking by Charlotte McCulloh

Collective Violence: Mob Formation of Lynchings by Ben Wallace

An Exceptionally Unified Front?: Convergence on China-related Issues in the U.S. Congress by Wanying He

The Rise of Doomscrolling: How Negative Emotions Drive Engagement with Political Content on Twitter by Jaiveer Katariya