Content
From social media and online shopping to self-driving cars and ChatGPT, digital technology is
ubiquitous in the social world. If the social sciences are to keep pace, then they must also
embrace computational methods and the digital world. This course will survey text analysis,
machine learning and social network analysis. We will use these tools to analyze a wide variety
of digital sources such as online text, images and metadata. We will also learn how
computational tools and digital data are changing the face of social science! This course has no
prerequisites and no programming experience is required. The course will introduce you to code
in several languages, but sample code and data will be provided. And as your instructor, I will
walk you through each exercise, step-by-step. No fear! Let’s start coding!
Audience
This course was designed for undergraduate and graduate-level social science majors.
Requirements
No prerequisites. Students will be expected to read and write at a graduate level.
Materials
- Syllabus – Introduction to Computational Social Science
- Module 1 sample code (zip) – Text Analysis
- Module 2 sample code (zip) – Machine Learning
- Module 3 sample code (zip) – Sematic Networks
YouTube Tutorials
- Text Analysis #1.1: Pre-processing
- Text Analysis #1.2: Sentiment Analysis
- Text Analysis #1.3: Keyword Analysis
- SVM Classification of Offensive Tweets
- Semantic Network Analysis (Bipartite)
Semesters Taught
- Spring 2024 – National Chengchi University
- Spring 2023 – KIMEP University
- Spring 2019 – Stanford University