Scientific Coordination

The Digital Revolution has produced unprecedented amounts of data that are relevant for researchers in the social sciences, from online surveys to social media user data, travel and access data, and digital or digitized text data. How can these masses of raw data be turned into understanding, insight, and knowledge? The goal of this course is to introduce you to Computational Social Science with Python, a powerful programming language that offers a wide variety of tools, used by journalists, data scientists and researchers alike. Unlike many introductions to programming, e.g., in computer science, the focus of this course is on how to explore, obtain, wrangle, visualize, model, and communicate data to address challenges in social science. The course emphasizes the theoretical and ethical aspects of CSS while covering topics such as web scraping (obtaining data from the internet), data cleaning and visualization, computational text analysis, machine learning, network analysis, and agent-based modeling. The course will be held as a blended learning workshop with video lectures focused on theoretical background and demonstrations accompanied by live online sessions where students can ask questions and work through projects together.

Organizational Structure of the Course

The course will take place in a blended learning format. That means that you will need to (1.) read the literature listed under each session (if any); (2.) watch the video lecture; (3.) finish the exercises before each live group session. This means that participants will be on roughly the same level of knowledge during the live sessions and we will be able to focus on open discussion, the answering of questions and small group exercises.

Target group

You will find the course useful if:

you have taken, e.g., a statistics course, know a little bit of Python, and now want to explore computational methods, data science or one of the approaches listed above.

Learning objectives

By the end of the course, you will:

be able to define what constitutes the field of computational social science;
have a high-level overview of the approaches utilized in computational social science, including advantages and shortcomings;
have a basic knowledge and hands-on experience of how to apply the approaches and what tools are considered state-of-the-art;
be equipped to deepen their knowledge on the theory and practice of computational social science.

Prerequisites

Working knowledge of Python is an asset, but is not required. There is an optional “Introduction to Python” module that you should review before beginning this course. Alternatively, you may consider taking the online workshop “Introduction to Python” that takes place from 26-29 August.

Software and Hardware Requirements

We will work with Python and Jupyter Notebooks in VS Code in the course, but you may use other text editors or IDEs if you prefer.

Schedule

Introduction to Computational Social Science

Self-learning Session 1: to be completed before Live Session 1 (see below), estimated workload: 3h

This session will give a broad overview of the field of computational social science, related fields and its development. The material will also provide background on the ethical conduct of CSS research and give participants practical guidelines on how to make sure their research adheres to ethical (and legal) standards. Additionally, it will provide basic skills in Exploratory Data Analysis (EDA) and visualization and going over how to set up things properly for the course.

Live Session 1: Friday, August 30th

In this session we will get to know each other. There will also be room for questions and we will make sure that everyone has the software up and running. We will then break into groups to work on a small task.

Obtaining Data

Self-learning Session 2: to be completed before Live Session 2 (see below), estimated workload: 3h

A core idea of CSS is that you work with found, rather than with designed data. Designed data would be data that was specifically collected through a survey or experiment, where the researcher controls what questions to ask and in what format the data is returned. Found data, on the other hand, are often traces left behind by people who were doing something that had nothing to do with research. Like writing, liking, sharing or deleting a post on social media, using a website, an app or a service or doing the jobs, for example, as politicians, journalists or book authors. These data can often tell us more about the actual behavior or people and institutions than what they would share in a survey or experiment. They are also often cheaply available on mass. But to download, wrangle and clean them can be difficult. This session gives and overview of web scraping, which is exactly the process of downloading, wrangling and cleaning data to make it possible to analyze it.

Live Session 2: Monday, September 2nd

After answering some questions, you will split up into breakout groups to work on a small web scraping project.

Computational Text Analysis

Self-learning Session 3: to be completed before Live Session 3 (see below), estimated workload: 3h

The advent of computational text analysis methods has reinvented the field of CSS. A lot of human interaction online is happening through textual data - at a scale that make efforts to manually analyze it to answer theoretical questions essentially impossible. Computational text analysis thus takes up a prominent role in CSS. In this session, you will get an overview of the most important approaches, namely dictionary methods, and supervised and unsupervised machine learning. We will also introduce deep and transfer learning with a focus on using generative Large Language Models (LLMs) for topic modelling and sentiment analysis.

Live Session 3: Tuesday, September 3rd

After answering some questions, you will split up into breakout groups to work on a small text analysis project.

Computational Network Analysis

Self-learning Session 4: to be completed before Live Session 4 (see below), estimated workload: 3h

Social and political network analysis has been heavily influenced by developments in computational social science, such as the availability of massive timestamped related data (e.g., from social media) and the development of new computationally-intensive modelling frameworks. This session will introduce you to the basic language, data, methods and models used in network analysis. We will emphasize working with social media data and generative approaches to inferential network analysis.

Live Session 4: Wednesday, September 4th

After answering some questions, you will split up into breakout groups to work on a small network analysis project.

Social Simulation & Agent-based Models

Self-learning Session 5: to be completed before Live Session 4 (see below), estimated workload: 3h

In network analysis, we analyze how different actors relate to each other in terms of what they say, do and are. In agent-based modeling, we simulate these connections, given a set of theoretical assumptions. This can be used to study social phenomena, such as a topic going viral, a social network becoming polarized or an actual disease spreading. If data is unavailable or would be unethical to obtain, agent-based models offer a valid approach to produce synthetic data to test theories.

Live Session 5: Thursday, September 5th

After answering some questions, you will split up into breakout groups to work on a small simulation project