CS 6724 - Computational Social Science
- Instructor: Tanushree (Tanu) Mitra
- Office hours:
- After class or by appointment at Torg 3160E
- Instructor Email: email@example.com
- Class hours: Tues, Thurs - 3:30pm to 4:45pm
- Where: Surge Space Building 103A
The increasing use of the Internet and online communities in the last decade has led to an explosion of social and behavioral data capturing every aspect of our daily activities – from what we like, what we read, to where we go, whom we know and beyond. This in-turn has led to the rise of computational social science – an emerging field which provides the opportunity to empirically study human behavior by applying computational methods, algorithms, and models on “big social data”.
In this course, students will adopt an interdisciplinary approach to empirically study different social phenomena with applications to social science fields, such as political science, sociolinguistics and sociology. The empirical approach will span a variety of quantitative methods, including applying existing machine learning tools and natural language processing techniques. In this seminar-style class, students will investigate several academic readings from this emerging field. They will also work on a semester-long research project in groups of 2-3 students. The goal of the project is to identify a question or problem that can be addressed by analyzing social data, with the broader goal of addressing larger societal issues such as ideological segregation, deviant behavior, online misinformation etc.
Course goals and learning objectives
After successful completion of this course, you will be able to:
- Describe the opportunities and challenges that the digital age creates for social research.
- Evaluate modern social research from the perspectives of both social science and computer science (data science).
- Create modern research proposals that blend ideas from social science and computer science.
- Practice the techniques needed to conduct proposed research to ultimately answer questions from a variety of practical scenarios and domains that matter to society at large, spanning politics, news, or health.
In terms of the required skills, students need to have basic knowledge of statistics, preliminary machine learning and a willingness to do interdisciplinary research. An overview of concepts and tools needed will be reviewed briefly in class, however in-depth coverage of the fundamentals is not in the scope of this course. This is NOT a machine learning or data mining course. Students also need to be proficient in programming. Experience in use of a scientific computing software like R is a bonus, but not required. Students should be prepared to apply what they have learned in prior computer science courses to this emerging new field. You are expected to quickly learn many new things. For example, your project may require you to fetch Twitter data using the Twitter API or analyze posts from Reddit using pre-existing libraries (like python nltk), which should not be too challenging if you already know high-level languages like Python. Please make sure you are comfortable with this.
No textbook is required for the course. Readings will be provided as linked pdfs or as electronic reserve from the Virginia Tech Libraries website, which means either being on campus or connecting to the VT network through a VPN.
The following sites will be used to support this class:
- This website, for syllabus and schedule.
- Class blog, for reading reflections. Please make sure to sign-up on vt wordpress
- Canvas, for assignments, grades, and submitting work.
- Piazza, for out-of-class discussion. Please make sure that you can access the class Piazza website.
- Class participation – 15%
- Reading reflections – 15%
- In-class paper presentation - 10%
- Term project – 60%
- Project pitch - 5%
- Project proposal - 5%
- Midterm project presentation - 10%
- Midterm milestone project report - 10%
- Final project presentation - 10%
- Final report - 20%
Your work will be graded on a list of criteria (specified on the assignment) such as quality of writing, completeness, insight into research issues, insight into social issues, etc. The general grading strategy is that for each criterion, you will receive either a check plus, check, or check minus. Most criterion will receive a check. A plus means “you impressed me.” A minus means the assignment is incomplete, incorrect, or sloppy in some fashion with respect to that criterion. Pluses and minues are combined to give your grade for the assignment. For most assignments, you start out half way between a B+ and A-. One plus makes it an A-; one minus makes it a B+. These are general guidelines to let you know what to expect. Grading on specific assignments may differ. Refer to the grading rubric whenever they are posted.
The final project serves as the final exam for this course. There is no separate exam.
Class participation (15%) - Individual (weekly twice)
You should actively take part in discussing the assigned readings and critically thinking about them during class to demonstrate that you have not only read the paper but you can also think outside the box. Attendance in class, participating in class discussions and in-class exercises and activities are critical and essential for success in this course. We will also do occasional quizzes spread across the semester. The purpose of the quiz is to ensure attendance and participation during class activities and also at times for me to gauge what topics we need to devote more time on. These quizzes will not be graded for correctness, but for completeness and participation. Note, the word – completeness. You cannot do a sloppy job in class and expect a grade for correctness on your quiz.
Reading reflections (15%) - Individual (weekly on average)
In this seminar-style class, you will investigate several academic readings, write your reflections where you will not just summarize the papers but think about what additional questions the paper enables, how is it relevant to modern digital social environments, give examples, talk about your experiences if any, be creative.
These are intended to facilitate and assess understanding of the reading materials. Reading reflections should be within one page (roughly within 600 words if you are using 12pt font). You won’t be penalized if you write more, but being succinct is another great writing skill which you should aim to cultivate in this course.
You do not need to summarize the full paper, but you need to reflect on what additional questions the work enables. Does this help you think about your next big project? What will that be? What other questions the paper makes you think? What else the paper is not answering or is concerning or is just intriguing?
Again this is an individual assignment and work submitted should be written solely by you. Most importantly, a reader while glancing at your reflection should be able to easily spot these questions. So use bold, italicize, bullet points or other means of highlighting them. Here is a great example of a reflection written by my colleague, Prof. Kurt Luther.
In-class paper presentation (10%) - Individual & Group (~ twice)
Students will lead paper discussions during the course of the study. The first set of papers will be led by students individually. I have listed these papers on the website. During the 2nd week of class, each student will be assigned a paper. The second set of papers will be presented by groups who are working together on a project. The goal is to allow you to pick a paper that serves as a model paper for your project – a paper that influenced you to come up with your project idea and helped you in executing your project. You have to get the instructor’s sign-off on the paper soon after your midterm project presentations.
Your paper presentation should include the following tasks:
- Prepare a presentation that highlights key claims of the paper, describes all figures and tables (using slides), and explain in detail the methods used in the paper. If possible, use Jupyter notebook to walkthrough the method of the paper. You will be given additional points if you are able to do so. Note that this may necessitate additional reading, reading of supplemental materials or additional research.
- Email two open-ended reading comprehension questions to the professor by 3pm on the preceding day (i.e. if you are presenting on Tuesday, send by 3pm Monday. If you are presenting on Thursday, send by 3pm Wednesday). These questions will be posed to the class. Subject of email: CS6724-[Date]-[Student Name]
- Lead the class discussion of the paper, soliciting comments from and posing questions to the other students.
- Stay within time limits. Refer to Canvas for a general timing guideline.
Term Project (60%) - Group (More Details)
The goal of the final project is to identify an interesting question or problem in the computational social science domain that you can address by analyzing social data and/or building social tools to augment current social systems. The papers and practicums discussed in class should help you along the way. Although project topics must be approved, you are free to pick a topic of interest in the general field of computational social science. You need to justify that the topic is interesting, relevant to the course, of suitable difficulty. Your project should have some some non-trivial analysis/algorithms/computation (e.g., computing basic statistics, like average, min/max will not be enough). You can also have an interactive user interface that interact with the algorithms (can be visual, browser–based, desktop–based etc.). Implementing your term project has multiple graded components starting with the project proposal and ending with the final presentation and report submission.
See the (Term Project) page for additional details.
Attendance and Participation
Participation accounts for 15% of your grade. Thus, if you are skipping classes, ignoring the readings, and/or failing to participate in class discussions, your participation grade will suffer.
Attending class is necessary to also successfully complete the course. We’ll often have in-class exercises and activities, and it’s expected that you will participate.
Readings will be assigned periodically, and these should be read before the next class, so that you can contribute to the discussion. Simply attending lectures will not be sufficient to fully understand the material.
- Reading Responses: No late days. All reading responses are due at 10am on the day of class. Responses to readings serve to stir class discussions, hence there is no point if you submit it at a later time after class.
- In-class paper presentation: No late days. All materials due at 10am on the day of your presentation.
- Midterm project presentation: No late days.
- Midterm project report: Late days allowed. However, late assignments will be penalized at a rate of one grade step (e.g., A becomes A-) per day. Submissions more than five days late will not be accepted.
- Final project presentation: No late days.
- Final project report: No late days. Due on the day of final exam.
The Virginia Tech Undergraduate Honor System is in effect for all work, whether performed individually or in teams. Be particularly careful to avoid plagiarism, which essentially means using materials (ideas, code, designs, text, etc.) that you did not create without giving appropriate credit to the creator (using quotation marks, citations, comments in the code, link to URL, etc.). Students are encouraged to consult with one another about project design and evaluation issues, as the sharing of ideas here will lead to better work. The final exam is entirely individual. Any suspected violations of the honor code will be promptly reported to the honor system, as required by university policy.
If you are a student with special needs or circumstances, if you have emergency medical information to share with me, or if you need special arrangements in case the building must be evacuated, please let me know privately as soon as possible.
Related Classes (Lots of materials adapted from them)
- Computational Social Science by Jacob Eisenstein
- Computational Social Science: Social Research in the Digital Age by Matthew Salganik
- Data Science and Analytics by James Caverlee