Topic outline

  • General

    Computational techniques have revolutionised social sciences leading to the fast-growing field of computational social science. While until recently it was only possible to use small-scale questionnaires studies and aggregated statistics to probe peoples’ lives, we can now have access to detailed logs of behavior of millions of people via the digital traces they leave in social media, mobile phones and other electronic means. Similarly, instead of theoretising how the different micro-scale behavioral patterns we observe in the data affects the macro-scale society using thought experiments and simplified models, we can now perform massive computer simulations to search for such emergent phenomena. In recent years, such techniques have been both used to test long-standing theories in social science but also to come up with completely new kinds of understanding of societies and individual behavior patterns. In addition to scientific research, social media platforms, game companies, online retailers and many other types of companies are using these techniques more and more to gain insight on their users behavior and competitive advantage. These analyses require a combination of techniques and ideas from computer science, applied mathematics and social sciences.

    In this course you will learn basic techniques and ideas of computational social science with the emphasis on computation. You will learn how to analyse data on detailed behavior large numbers of people and draw conclusions on the system level behavior that emerges from it. You will build simulations of artificial societies, and see how various societal phenomena, such as segregation, inequality and polarisation, can emerge from individual behavior patterns that might seem relatively insignificant at first sight. Finally, as the ideas and techniques you learn are extremely powerful, we will discuss issues related to ethics and privacy in relation to computational social science research and practices.


    First lecture: Monday 27.2. at 10.15 in lecture hall M1 (Undergraduate Centre)


    Exercises


    To access and submit the exercises, please use the A+ system: https://plus.cs.aalto.fi/cs-e4730/2023spring/

    The course will open in A+ at 10.00 on 27.2.


    You can ask for help in solving the exercises in the Friday exercise help sessions and the course Zulip chat: https://css2023.zulip.aalto.fi/join/ezubrlv3quxurhink2heszxd/


    Schedule

    Period IV

    Week   

    Lecture   

    Exer. dl   

    Ext. dl    

    Topic

    1

    Feb 27

    Mar 3

    Mar 15

    Introduction 

    2

    Mar 6

    Mar 10

    Mar 22

    Artificial societies and agent-based models 

    3

    Mar 13

    Mar 17

    Mar 29

    Digital traces and data collection

    4

    Mar 20

    Mar 24

    Apr 5

    Counting things and analysing text

    5

    Mar 27

    Mar 31

    Apr 12

    Social networks: structure


    6

    Apr 3

    -

    -

    Introduction to project


    Period V

    Week   

    Lecture   

    Exercise dl   

    Ext. dl    

    Topic

    7

    Apr 24

    May 5

    May 10

    Ethics, privacy, legal

    -

    -

    -

    -

    WAPPU

    8

    May 8

    May 12

    May 24

    Agent-based models, emergence

    9

    May 15

    -

    -

    Social networks: dynamics

    10

    May 22

    -

    -

    Experiments & interventions at scale

    11

    May 29

    -

    -

    Computing for social good


    Project deadline: May 26

    Project peer review: June 2

    Grade limits (adjusted to be slightly easier than the ones announced in lecture 1)
    1 : 740,
    2: 888,
    3: 1036,
    4: 1184,
    5: 1410

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