教學大綱 Syllabus

科目名稱：計算社會科學導論

Course Name: Introduction to Computational Social Science
  • 學年學期：112-2 Spring Semester, 2024
  • 科目代碼：ZU1880001 Course No.ZU1880001

修別：選

Type of Credit: Elective

3.0

學分數

Credit(s)

20

預收人數

Number of Students

課程資料Course Details
  • 開課單位：創國學一、創國學二、創國學三、創國學四 Course Department:Global Studies/B/1&2&3&4
  • 授課老師：ReidheadJacob Instructor: REIDHEAD JACOB
  • 先修科目：無Prerequisite(N/A)
  • 上課時間：一D56 Session: mon13-16

課程簡介Course Description

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!

核心能力分析圖 Core Competence Analysis Chart

能力項目說明

    課程目標與學習成效Course Objectives & Learning Outcomes

    Learning Outcomes

    Through this course you will become familiar with:

    1. computational tools and digital data
    2. the ways computational tools and digital data are impacting the social sciences

    And you will practice:

    1. Implementing computational methods on sample code and data
    2. Communicating your computational analyses and their broader applications

     

    Modules

    The course is organized around an introductory section and three methodology modules:

    1. Ethics and AI
    2. Module 1: Text Analysis
    3. Module 2: Machine Learning
    4. Module 3: Social Network Analysis

     

    Teams

    By the second week of the semester, the class will organize into teams of 2 or 3 students. Students will work in teams on team projects and video presentations.

     

    Activities

    In class, students will actively participate in:

    • Lectures & In-class Activities

    Out of class, students will work individually on:

    • Coding Tasks
    • Research Reviews

    Out of class, students will work in teams on:

    • Team Videos

    每周課程進度與作業要求 Course Schedule & Requirements

    教學週次Course Week 彈性補充教學週次Flexible Supplemental Instruction Week 彈性補充教學類別Flexible Supplemental Instruction Type

    Week

    Topic

    Content and Reading Assignment

    Teaching Activities and Homework

    1

    Ethics of Big Data and Computational Methods

    AI Prompt Engineering

    AI Prompt Engineering

    2

    ChatGPT and AI prompt engineering

    AI Prompt Engineering

    AI Prompt Engineering

    3

    Survey of Text Analysis

    Text Analysis

    Methods & Applications

    Code #1a – NLP Prep

    4

    Keyword and Latent Topic Analysis Tutorial

    Text Analysis

    Methods & Applications

    Research Review #1

    5

    Sentiment Analysis Tutorial

    Text Analysis

    Methods & Applications

    Code #1b – NLP Analysis

    6

    Text Analysis in Research

    Text Analysis

    Methods & Applications

    Team Video #1

    7

    Team Video Roundtable #1

     

     

    8

    Survey of Machine Learning

    Machine Learning

    Methods & Applications

    Code #2a – ML Prep

    9

    Clustering Tutorial

    Machine Learning

    Methods & Applications

    Research Review #2

    10

    Classification Tutorial

    Machine Learning

    Methods & Applications

    Code #2b – ML Analysis

    11

    Machine Learning in Research

    Machine Learning

    Methods & Applications

    Team Video #2

    12

    Team Video Roundtable #2

     

     

    13

    Survey of Social Network Analysis

    Social Network Analysis

    Methods & Applications

    Code #3a – SNA Prep

    14

    Node-Level Analysis Tutorial

    Social Network Analysis

    Methods & Applications

    Research Review #3

    15

    Network-Level Analysis Tutorial

    Social Network Analysis

    Methods & Applications

    Code #3b – SNA Analysis

    16

    Social Network Analysis in Research

    Social Network Analysis

    Methods & Applications

    Team Video #3

    17

    Dragon Boat Festival

    Dragon Boat Festival

    Dragon Boat Festival

    18

    Team Video Roundtable #3

     

     

    授課方式Teaching Approach

    40%

    講述 Lecture

    20%

    討論 Discussion

    40%

    小組活動 Group activity

    0%

    數位學習 E-learning

    0%

    其他： Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    Grading Rubric

    Task

    Points per Assessment

    Percent of Semester Grade

    Attendance

    After TWO FREE ABSENCES, each unexcused absence is -5%

    10%

    Coding Tasks

    6 tasks * 5% each

    30%

    Research Reviews

    3 reviews * 10% each

    30%

    Team Videos

    3 videos * 10% each

    30%

     

    Coding Tasks – 30%

    A coding task will be assigned for Modules 2-7. The instructor will provide tutorials, sample code and data. Students will watch the tutorial and execute the sample code, making small changes, as the task requires. Students will have at least two weeks to complete each coding task and upload the results to Moodle.

     

    Research Reviews - 30%

    For each of the three modules, a research review will be assigned. The instructor will specify a computational method or type of data. Students will be asked to find three research papers using that type of method or data, and then to summarize and compare the three papers in a 2-page research review. Students will have at least one month to complete each review and upload it to Moodle.

     

    AI Project and Team Video – 30%

    For each of the three modules, students will work in teams to complete an AI project and make a 10-minute video presenting their results. Each project will expand on that module’s coding task and research review. Working in teams, students will be asked to use a current AI tool in order to implement a computational method. Then they will create a team video, also using AI tools, if they wish, to present the results of their project.

     

     

    Generative AI

    Students are encouraged to use generative AI to augment any aspects of all assignments including literature reviews, coding and team videos. If AI-generated results do not fully satisfy assignment criteria, some human intervention may be required in order to complete the assignment and receive full credit.

    指定/參考書目Textbook & References

    Text Analysis

    • Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press.
    • Bengfort, B., Bilbro, R., & Ojeda, T. (2018). Applied text analysis with Python: Enabling language-aware data products with machine learning. " O'Reilly Media, Inc.".
    • Szabó, G., Polatkan, G., Boykin, P. O., & Chalkiopoulos, A. (2018). Social media data mining and analytics. John Wiley & Sons.

     

    Machine Learning

    • Jacobucci, R., Grimm, K. J., & Zhang, Z. (2023). Machine Learning for Social and Behavioral Research. Guilford Publications.
    • Beyeler, M. (2017). Machine Learning for OpenCV. Packt Publishing Ltd.

     

    Social Network Analysis

    • Borgatti, S. P., Everett, M. G., Johnson, J. C., & Agneessens, F. (2022). Analyzing social networks using R. Sage.
    • Jackson, M. O. (2008). Social and economic networks (Vol. 3). Princeton: Princeton university press.

    已申請之圖書館指定參考書目 圖書館指定參考書查詢 |相關處理要點

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    課程相關連結Course Related Links

    Moodle Link - TBD

    課程附件Course Attachments

    課程進行中，使用智慧型手機、平板等隨身設備 To Use Smart Devices During the Class

    是 Yes

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