Fall 2020

The course provides an overview to the emerging field of computational social science. Through exploring computational social science methods and their use in social sciences today, this course helps students to engage with questions on research design. We focus on four methodological approaches: machine learning, network analysis, simulations and interactive systems research methods. Beyond this, we discuss ethics, validity and integration of computational social science students.

The goal is that

  1. students are able to develop research designs which benefit from computational social science methods
  2. students are able to critically evaluate research plans or published papers which use computational social science methods in terms of feasibility, validity, reliability, ethics and contribution.

Administrative details

For admnistrative details and enrollment, see

Course elements

Pre-readings help students to orient to class content and allows us to have more indepthful discussions. We will use the book "Coding Social Science. Understanding and Doing Social Science."

Lectures are not mandatory but I make no guarantees that the slides make sense without being present in the lecture (i.e.; I'll aim to make presentation slides) nor can the student join the intellectual discussions around the theme. Thus, I encourage studetns to join the lectures.

Each student writes learning diary

  1. Before each class, students should write a minute paper; that is, write a brief responses to questions
    • What are the three most central things you have learned by reading this chapter?
    • What questions remain uppermost in your mind?
    • Is there anything you did not understand?
  2. After each lecture to summarize what was the key take-home-message of the lecture and any open questions after the lecture.

When writing a learning diary, avoid repeating what was said and rather weight your own opinions. We will use an online platform for these discussions and students are expected to comment the after-lecture notes by providing their insights and comments to the posts.

Plase limit the length of learning diary to 250 words just to give an idea of the size of contribution (which is small) in these learning diaries. To foster the interaction, it is mandatory to respond to at least one after-lecture notes (and I will also engage the discussion). If you miss a class, kindly write at least three comments to the posts. (No need to write an after-lecture summary, naturally.)

Also students are expected to return a final assignment see below.

Course evaluation

20% of the grade is based on the lecture diary.

80% of the grade is based on final assignment.

For PhD students, the focus is on quality and innovativeness of the research proposal. I will use the Academy of Finland review form for the evaluation.

For Masters' student, the focus is on demosntration that you understand what takes place in the paper and can reflect those further. We will co-develop an evaluation matrix during the first draft stages of the paper.

Syllabus

Lecture 1: Introduction (4.9.)

Read Chapter 1 before the class.

Lecture 2: Algorithmic data analysis (18.9.)

Read Chapter 3 before the class.

Lecture 3: Network analysis (2.10.)

Read Chapter 4 before the class.

Lecture 4: Simulation & Complex systems (16.10.)

Read Chapter 6 before the class.

Lecture 5: Constructing interactive systems (30.10.)

Read Chapter 7 before the class.

Lecture 6: Validity and ethics (13.11.)

Read Chapters 9 and 10 before the class.

Lecture 7: Integrating computational methods into a research project (27.11.)

Read Chapter 11 before the class.

Final assignment and deadlines

Master's students

Summarise three papers which use computational social science methods. Choose papers so that they use three of the four different method families discussed in the book. Find the papers yourself, do not use articles presented or discussed as cases from the book. In each summary

  1. what is the research question authors explicate and how do they motivate it
  2. tell what did authors do in the paper in terms of methods, data and results
  3. what kinds of archetypes of computational social science is present in the book
  4. what concerns of ethics and validity relate to the article
  5. what kind of trading zones are present in the paper
  • First draft of the first summary: 14.10. 12:00
  • First draft of the second summary: 28.10. 12:00
  • First draft of the third summary: 11.11. 12:00
  • Revision of all summaries: 29.11. 12:00
  • Final deadline of all summaries: 14.1.2021 12:00

You will recieve instructor and peer-feedback of drafts and revisions to help you improve the summaries.

For PhD students

An important skill of a PhD is to apply theorethical conncepts and methods into research problems they find relevant and interesting. To train these skills, we will write a research proposal where they apply the methods or concepts presented in this course to a research question or problem they find interesting.

To help in structuring the research proposal, the Postdoctoral Researcher applications format from Academy of Finland. (It is fairly good summary how a research proposal might look like.) Follow the Academy of Finland guidelines on content, length etc. Naturally, you won't be using the Academy of Finland online template, but submit them in a single document.

  • First draft of the "Aims and Objectives" on the Academy of Finland template) 14.10. 12:00
  • First draft of "Implementation" on the Academy of Finland proposal 11.11. 12:00
  • First draft of "Responsible science" and "Societal effects and impact" and revision of previous sections 29.11. 12:00
  • Final deadline for the research plan 14.1.2021 12:00

You will recieve instructor and peer-feedback of drafts and revisions to help you improve the summaries.