Fall 2019

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. 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.

Course elements

Pre-readings help students to orient to class content and allows us to have more indepthful discussions. This year, you'll mostly read book chapters from forthcoming text book "Coding Social Science. Understanding and Doing Social Science." This is a text book I'm currently writing and I want to see how sections in the book are working for students.

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.)

As an final assigment, you 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. This is motivated by the academy of Finland application forms for early career researchers. You must submit a Postdoctoral Researcher applications and reguired appendixes. 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.

If you're not a PhD student, we can discuss alternative ways of doing this as well.

Course evaluation

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

80% of the grade is based on the quality and innovativeness of the research proposal. I will use the Academy of Finland review form for the evaluation.

Syllabus

Lectures 1: Introduction

Goal of lecture

Lecture 2: Data science

Lecture 3: Network analysis

Lecture 4: Simulation & Complex systems

Lecture 5: Interactive applications for research

Lecture 6: Validity and ethics

Lecture 7: Integrating computational methods into a research project

Research proposal

Non-mandatory commenting and peer-commenting session

TBA

Final deadline for research proposal

TBA