DTU 02467 Computational Social Science Course Spring 2023
Before week 1. Take a look at this page before you do anything. This class most likely works a little bit differently from other classes you’ve taken. The notebook explains pretty much everything - the rest will be explained during the lectures. In case the link doesn’t work, you can also see the file here on Github, but the videos won’t display properly
Week 1: Intro to Computational Social Science. This week is all about getting started: learning about how the course works, make sure you master the tools you need to follow the class. I will give you an introduction to the field of Computational Social Science. And, we will start with something hands-on: you will learn about web-scraping and start gathering some data from the web.
Reading: Bit by Bit, chapter 1 Start by reading the Introduction of the book, where you will get you an understanding of the history of the field and the general framework.
Reading: Bit by Bit, chapter 6 Read the Ethics chapter of the book. Here, I don’t expect you to read all the details. However, I want to make sure you get an overall understanding of the ethical challenges and some of the approaches that are used in the field to deal with these complex issues. You can focus on sections 6.4 and 6.6.
Week 2: Data 1 - Gathering data This week we will learn more about data sources for Computational Social Science. First you will learn about different data types by listening to some theory and reading the book. Then we will move on to do something practical: you will learn about APIs to collect data and use them to gather some (actually a lot!) of data.
- Reading: Bit by Bit, sections 2.1 to 2.3 Read sections 2.1 to 2.3. The idea is for you to understand, in general terms, advantages and challenges of large observational datasets (a.k.a. Big Data) for social studies.
Week 3: Data 2 - Distributions in empirical data This week we will focus on two topics. First, we will talk about how to effectively use data visualization techniques as a tool to analyse empirical data. Then, you will learn about heavy tailed distribution, which are common in many real-world social datasets. You will hear some theory and put things into practice through exercises.
- Reading: Power laws, Pareto distributions and Zipf’s law Read the introduction and skim through the rest of the article.
Week 4: Networks I - Building the Network Today we learn about networks and how we can use them to understand social systems. We will start from the theory. There will be some video lectures + some reading, where we’ll answer some important questions, such as “Why would anyone care about networks” and “How can you use Python to study networks”. Then, we will put the learning into practice. We will build the social network of Computational Social Scientists and run some initial analyses.
Assignment: Assignment 1 is live!
Week 5: Networks II - Properties of Real World Networks. More on networks! First some talking by me, where you will learn some of the properties of real-world social networks. Then, you will use the NetworkX library to visualise and investigate the properties of the Computational Social Scientists Network. You will study properties of this network and compare it to a random network model.
- Reading: Chapter 3 of the Network Science book. The most important sections are 3.1 to 3.4 and 3.8 to 3.10, so focus on that.
Week 6: Networks III - Centrality, Assortativity and Communities. Today we will study the network of Computational Social Scientists more in depth. We will learn about more advanced network science concepts (centrality, assortativity, communities), then put things into practice to study the network of Computational Social Scientists.
- Reading: Chapter 7 of the Network Science book(the most important sections are 7.1 to 7.3); Chapter 9 of the Network Science book (you can skip 9.3, 9.5 and 9.7).
Week 7: Text 1 - The Basics . We’re changing gears. We’ve looked at the network of Computational Social Scientists. Now we’ll put together the tools for working with the text. We will learn the basics, then get real and work with the text from the papers’ abstracts.
- Reading I: Chapter 1 (sections 1.1 to 1.3) of the Natural Language Processing with Python (NLPP) book.
- Reading II: Chapter 3 (sections 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.9, and 3.10. ) of the Natural Language Processing with Python (NLPP) book It’s not important that you go in depth with everything here - the key think is that you know that Chapter 3 of this book exists, and that it’s a great place to return to if you’re ever in need of an explanation of regular expressions, unicode, etc.
Week 8: Text 2 - Networks and Text . Today we will talk a bit more about basic techinques to explore textual data and will apply to study the abstract dataset. It is a pretty light class, so you have time to focus on the assignment.