2025 (Current Year) Faculty Courses School of Environment and Society Department of Technology and Innovation Management Graduate major in Technology and Innovation Management
Computational Social Science
- Academic unit or major
- Graduate major in Technology and Innovation Management
- Instructor(s)
- Kazutoshi Sasahara
- Class Format
- Lecture/Exercise
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- TIM.B536
- Number of credits
- 0.50.50
- Course offered
- 2025
- Offered quarter
- 3Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
Computational social science is a social science of the digital age, made possible by the utilization of big data and computers. It employs big data and mathematical methods and information technologies to quantitatively study individuals, groups, society, and economy with unprecedented resolution and scale. This lecture aims to help students acquire the theories and methodologies of computational social science and develop the foundations for applying them to solving real-world problems and creating innovation.
Course description and aims
The objectives of this lecture are as follows:
- Understand the theories of computational social science and be able to apply them to solving real-world problems
- Acquire the major analytical methods of computational social science, such as machine learning, social network analysis, and text mining, and develop skills to apply them to real-world data
- Understand the latest research methods such as virtual labs and survey experiments, and become able to design and conduct experiments using digital tools
Keywords
Machine learning, crowdsourcing, social network analysis, generative AI, social media, virtual lab, big data
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The class will be conducted interactively with discussions. Also, while incorporating insights from the latest academic research, it will emphasize the balance between theory and practice. By combining lectures, practical training, and group work, students will learn proactively and develop the ability to apply computational social science methods to real-world problems.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction to Computational Social Science | Learning about the history, definition, purpose, and methodological overview of computational social science. Deepening understanding of the interdisciplinary approach that analyzes social phenomena using large-scale data and computational methods. |
| Class 2 | Big Data and Humans, Society, and Economy | Learn characteristics and analytical methods of big data, and exploring how they can be used to understand human behavior, social phenomena, and economic activities. Also acquiring practical skills in data collection, processing, and analysis. |
| Class 3 | Social Network Analysis | Learn methods to analyze relationships and interactions between people and organizations. Understanding the impact of network structure and dynamics on society and economy, and conducting practical analysis exercises using actual data. |
| Class 4 | Text as Data | Learn methods to extract useful information from large amounts of text data using natural language processing and machine learning. Furthermore, mastering causal inference with text data and analysis using embedding representations, and deepening understanding through exercises using real data. |
| Class 5 | Virtual Laboratory | Learn experimental research methods conducted online, and acquiring the design and implementation methods for actual virtual experiments. Also discussing ethical considerations and ensuring data quality. |
| Class 6 | Survey Experiments | Learn the design and analysis methods of survey experiments, which incorporate experimental elements into traditional survey techniques. Deepening understanding of the fundamentals of causal inference and how to implement it through survey experiments. |
| Class 7 | Latest Trends in Computational Social Science (Generative AI, etc.) | Introducing research findings and case studies on new methods of social science research utilizing generative AI, and discussing the application possibilities and issues of the latest technology for computational social science. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Slides will be provided.
Reference books, course materials, etc.
- Filippo Menczer, Santo Fortunato, Clayton A. Davis, A First Course in Network Science, 2020
- Matthew J. Salganik, Bit by Bit: Social Research in the Digital Age, 2017
Evaluation methods and criteria
Class contribution 10%, Exercise 30%, Report 60%
Related courses
- TIM.A510 ： Social Simulation I
- TIM.A511 ： Social Simulation II
- TIM.A414 ： Introduction to Models and Experiments in Social Science
- TIM.A405 ： Methodology of Mathematical and Computational Analysis I
- TIM.A406 ： Methodology of Mathematical and Computational Analysis II
- TIM.B535 ： Digital Marketing
Prerequisites
None