| Academic Year |
2026Year |
School/Graduate School |
Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Social Data Science Program |
| Lecture Code |
WMK00500 |
Subject Classification |
Specialized Education |
| Subject Name |
計算社会科学 |
Subject Name
（Katakana） |
ケイサンシャカイカガク |
Subject Name in
English |
Computational Social Science |
| Instructor |
KAJIKAWA HIROAKI,HARADA YUSUKE,SUZUKI YOSHIHISA |
Instructor
(Katakana) |
カジカワ ヒロアキ,ハラダ ユウスケ,スズキ ヨシヒサ |
| Campus |
Higashi-Senda |
Semester/Term |
1st-Year, First Semester, First Semester |
| Days, Periods, and Classrooms |
(1T) Tues11-12：Online, (2T) Weds11-12：Online |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Online (simultaneous interactive), Online (on-demand) |
| |
| Credits |
2.0 |
Class Hours/Week |
2 |
Language of Instruction |
J
:
Japanese |
| Course Level |
1
:
Undergraduate Introductory
|
| Course Area（Area） |
24
:
Social Sciences |
| Course Area（Discipline） |
05
:
Sociology |
| Eligible Students |
1st-Year, 1st-Semester, 1Term |
| Keywords |
Computational social science, big data analysis, digital surveys, digital experiments, data collection, networks, text analysis, machine learning, social simulation, statistical modelling, social physics, ethics |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
In this course, students will learn how to analyse and understand social phenomena using data and computational techniques. Through methods such as big data analysis, digital research and computational modelling, students will explore new solution approaches to the complex challenges of contemporary society.
Emphasizing both theoretical understanding and practical skills, the course aims to provide students with the basic knowledge and skills necessary to explore future research, as well as to acquire comprehensive skills with an eye to their use in business. |
| Class Schedule |
lesson1 Guidance: background to the birth of computational social science, characteristics of big data, related areas, data-driven approaches
lesson2 Web and SNS surveys: social science and web surveys, ethics of surveys, characteristics of survey data and points to note
lesson 3 Digital experiments: types and characteristics of digital experiments (browser add-ons, smartphones, social media, etc.)
lesson 4 Data collection and use of public data: what is research data, types and examples of public data, points to bear in mind when using data, ethics of data
lesson 5 Network analysis: basic knowledge of network data, feature analysis, node feature analysis
lesson6 Text analysis (text as data): features of quantitative text analysis, pre-processing of text, methods of text analysis, research examples
lesson 7 Supervised machine learning for social data analysis: computational social science and l-machine learning, supervised machine learning, supervised data pre-processing, prediction accuracy.’
lesson 8 Social simulation: overview, history, representation and implementation of social simulation models, examples
lesson 9 Statistical modelling: statistical modelling, properties of behavioural big data, modelling with behavioural big data.
lesson 10 Sociophysics: what is in-house physics, the great manifold model, opinion dynamics, simulation of society
lesson 11 sessions Ethics in computational social science: ethics in scientific research, magnetic physics
lesson 12 Research in computational social science: introduction of researchers and research topics
lesson 13 Text analysis exercise 1: text decomposition using Python, data pre-processing
lesson 14 Text analysis exercise 2: visualization of text decomposition, text mining
lesson 15 Summary (future developments and issues) |
Text/Reference
Books,etc. |
Introduction to Computational Social Science Edited by Fujio Toriumi |
PC or AV used in
Class,etc. |
Text, Microsoft Teams, moodle |
| (More Details) |
|
| Learning techniques to be incorporated |
Discussions, Quizzes/ Quiz format, Post-class Report |
Suggestions on
Preparation and
Review |
1st-15th:
(Preparation) Reading of literature
(Review) Summary of comments from discussions and related materials |
| Requirements |
|
| Grading Method |
[Conditions for credit recognition].
◎ Watching the videos on Moodle and submitting all the assignments as instructed by the deadline.
◎Submit a final report in accordance with the instructions.
Even if all assignments and reports have been submitted, credits may not be awarded according to the following [Grading] criteria.
Grading
Evaluation will be based on the reports of each session. |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
If the assignment is not submitted, the grade for that session will be zero. Please note the deadlines and submission instructions for each session. |
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |