WMK00500 Computational Social Science

Hiroshima University Syllabus

Japanese

Academic Year 2025Year 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 (1st) Tues11-12：Online

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, 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 This course is offered on demand as a special measure for working students and students who have difficulty commuting to school.
(1) Students who are conducting their main research at a campus other than Higashihiroshima Campus, Kasumi Campus or Higashi Senda Campus during the term.
(2) Students who are working adults.
(3) Students who have difficulty commuting during the term due to childcare or other family circumstances.
(4) Those who are unable to commute during the term due to injury or illness.
(5) Those who have difficulty in completing their studies within the term of study due to the curriculum of the degree programme to which they belong (only when the head of the programme approves).
The following documents must be submitted to the Education Promotion Group (in charge of Liberal Arts Education) during the ‘Course Registration Period’ and ‘Course Confirmation/Adjustment Period’ of the term in question.
Reasons for course registration.
Documents that objectively show that the conditions apply to the course.
The videos will be distributed via the online learning system ‘Moodle’.
◎ Instructions will be given separately after course registration is completed regarding course methods, video viewing periods, assignment submission deadlines, and other course-related matters.
◎If you have any questions about the course content, please contact the course instructor (email address above) directly by email.

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.

Academic Year	2025Year	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	(1st) Tues11-12：Online
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, 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	This course is offered on demand as a special measure for working students and students who have difficulty commuting to school. (1) Students who are conducting their main research at a campus other than Higashihiroshima Campus, Kasumi Campus or Higashi Senda Campus during the term. (2) Students who are working adults. (3) Students who have difficulty commuting during the term due to childcare or other family circumstances. (4) Those who are unable to commute during the term due to injury or illness. (5) Those who have difficulty in completing their studies within the term of study due to the curriculum of the degree programme to which they belong (only when the head of the programme approves). The following documents must be submitted to the Education Promotion Group (in charge of Liberal Arts Education) during the ‘Course Registration Period’ and ‘Course Confirmation/Adjustment Period’ of the term in question. Reasons for course registration. Documents that objectively show that the conditions apply to the course. The videos will be distributed via the online learning system ‘Moodle’. ◎ Instructions will be given separately after course registration is completed regarding course methods, video viewing periods, assignment submission deadlines, and other course-related matters. ◎If you have any questions about the course content, please contact the course instructor (email address above) directly by email.
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.

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