The goals of this seminar class are twofold: first, students will be acquainted with computational tools and techniques for processing and thinking about social data, and be exposed to papers spanning the full spectrum of computational social science methods from large-scale empirical data analysis to online experimetation. Second, students will develop research skills by reading, reviewing, presenting, and discussing recent academic papers.
Every week, we will cover a different computational social science topic by reading two papers and discussing them. Before class, everyone will write a review of the papers, identifying their research questions, strengths and weaknesses, and connection to other literature. Each paper will be assigned to 2-3 people who will lead a group discussion of it in class. Throughout the term, everyone will get the chance to present one paper.
The major coursework component of the course, besides the weekly reviews, will be a term project. Students will propose a topic, give a presentation on their work, and submit a final report. The project will give students a chance to identify an interesting computational social science problem and implement it, and could potentially lead to publication in a workshop or conference.Grading scheme:
We will read the brand new book Bit By Bit: Social Research in the Digital Age by Matthew Salganik. It's available online for free, and in print form at a reasonable cost.
|Week||Date||Topic||Reviews Due||Textbook Readings|
|1||1/14||Introduction to computational social science||Ch. 1|
|2||1/21||Introduction to computational social science cont'd||Ch. 1|
|3||1/28||Observational studies 1||1/27 9:00pm||Ch. 2|
|4||2/4||Observational studies 2||2/3 9:00pm||Ch. 2|
|5||2/11||Experiments 1||2/10 9:00pm||Ch. 4|
|6||2/25||Experiments 2||2/24 9:00pm||Ch. 4|
|8||3/11||Asking questions||3/10 9:00pm||Ch. 3|
|9||3/18||Mass collaboration||3/17 9:00pm||Ch. 5|
|10||3/25||Ethics in computational social science||3/24 9:00pm||Ch. 6|
|11||4/1||Project presentations (Part 1)|
|12||4/8||Project presentations (Part 2)|
Students will present their project proposals.
Students will present their final projects.
The main point of this class is to engage with important and cutting-edge research at the interface of computer science and the social sciences. This involves reading, reviewing, discussing, and presenting papers. Reviewing papers for CSC2552 will help you develop your reviewing and critical thinking skills, as well as prepare you for in-class discussions. In what follows, I've written some thoughts on how to write a good review. Every week that we have a discussion class, your paper reviews will be due on Wednesday at 9pm before class.
Your reviews in this class are meant to be similar to real reviews that you would write in a peer-review process, but they will be somewhat different. When you are reviewing for a real conference or journal, the main point of the review is to determine if it should or should not be published in that venue. In this class, we will be reviewing established published papers, so the main point will be to think critically about them. There will be less emphasis (but not no emphasis) on the soundness of the results, and more emphasis on the decisions the researchers made, the interpretation of the results, and the implications of the research.
As part of your review, provide a concise (1-2 sentence) summary of the paper. What is the main result? This demonstrates that you understood the high-level point of the work, and it's often a useful exercise to circumscribe the domain the paper is exploring.
List the strengths and weaknesses of the paper. These should be major pros and cons, not little nitpicks. It's extremely rare that a paper doesn't have both several strengths and several weaknesses. Research is difficult, and there are almost always tradeoffs. What are the compromises the authors made? What is good about their approach, and what is bad?
Then the main part of the review will be a discussion and response to the work. What exactly have the authors shown? Do you agree with their interpretation of the results? Do the strengths of their approach justify the weaknesses? Should the authors have done anything differently, in your opinion? What else should they have done? What are the implications of the results? How does this research inform or compliment other work in computational social science, or society in general?
Reviews should be concise. Papers are "big" things; they represent an entire research project that a group of people have spent significant time pursuing. Resist the temptation to address every detail, or go off on a small tangent. Keep to the main and most important points. Reviews should not be longer than 500 words.
One of the main goals of CSC2552 is to introduce you to research in computational social science. The best way to do that is to get your hands dirty and try to study something yourself, and the final project offers you an opportunity to do exactly this.
The project has two main deliverables: the project proposal and the final report. Students will present each to the class (proposals on March 4 and final reports on April 1 and April 8). Projects will be done in teams of one or two people.
Your first task is to pick a project topic. If you are looking for project ideas, please email me, and I'd be happy to brainstorm and suggest some project ideas. Also check this resources page.
Project proposal (2-3 pages). The project proposal should outline your research question(s), survey two related papers, and outline your research plan. Try to articulate your research question as crisply as you can. What is the big one-sentence question that you are trying to answer? Next, pick two (or more) related papers to your project and discuss them much as you would in a review for this class. What are the research questions they posed, the strengths and weaknesses of their approach, and what are your thoughts on the work? Finally, the proposal should lay out a plan for your project. How exactly do you plan to pursue your research questions? What data/methods do you plan to use? You should try to provide a concrete proposal for a model, algorithm, or analysis that potentially extends or improves the topics discussed in the papers you've read.
Thanks to Sharad Goel, Jon Kleinberg, Jure Leskovec, Matt Salganik, Johan Ugander, and Bob West for course advice and inspiration.