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Welcome to the wiki for the course Computational Social Science (02467) offered by the Technical University of Denmark. This is the main page, where you can access the weekly exercises. If you take a look in the side-bar, you can read about the administrative details (including a very useful course overview), assignments, books, and more.
The class is taught flipped classroom style, where the the lecture and homework elements of a course are reversed. You'll be able to view short video lectures during the class session, so in-class time can be devoted to exercises, projects, or discussions. Check out the Before Week 1 lecture to learn more.
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: Introduction. This week is all about getting started: learning about how the course works, make sure you master Python and Pandas, and learn what is Computational Social Science. We will also start having some fun with data: We will download data using the Reddit API and analyse it. In case the link below doesn't work, you can also see the file here on Github, but the videos won't display properly.
- Reading: Bit by Bit, sections 2.1 to 2.3 Read sections 2.1 and 2.3, then skim through section 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 2: Intro to Data Visualisation. This week we will learn the basics of Data Visualisation, first by reading and listening to some theory, then with some practical exercises. We will use exploratory data visualisation to figure out how Redditors engaged in discussions around GME throughout last year, and get some understanding of how the online discussion related to changes in the stock price.
Week 3: Networks I . It's time to 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 redditors talking about GME on r/wallstreetbets and run some initial analyses.
Week 4: Networks II . 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 GME Redditors 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 5: NLTK I. Ok. So we're changing gears. We've looked at the networks in Reddit. Now we'll put together the tools for working with the text. This first week is going to be a walk in the park - so we'll just be installing the software, reading the book a bit, and solving some exercises. Easy-peasy. (Plus, if you're behind, today's light load makes it a nice day to catch up on everything else).
Week 6: NLTK II.. Let's get real and work with some language/text. We will go through the dreaded chapter 3 of NLPP, talk about TF-IDF as a way to summarise what is important about a document. And finally, we will work with the text from the r/wallstreetbets submissions.
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Reading I: NLPP Chapter 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.9, and 3.10. 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.
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Reading II: Check out the wikipedia page for TF-IDF.
Week 7: NLTK III.. We continue to work language/text. First, we will learn about Sentiment Analysis, and how do we figure out if a text express positive or negative sentiments. We will then apply Sentiment Analysis to study opinions in r/wallstreetbets over time.
- Reading: There is not much to read this week. In the class, we will use material from this paper, titled Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter. I suggest you to skim through it, especially the abstract and figures. You don't have to read it all.
Week 8: Networks III.. We will now go back to networks. This week, we will learn about Community Detection, which is a way to discover structures of highly interconnected nodes in a network. There will be some theory and then some exercises, just as usual :)
- Reading: Chapter 9 of the Network Science book (You can skip 9.3 and 9.5).