Coursebooks 2017-2018


Topics in Computational Social Science


Lecturer(s) :

West Robert




Every year


Next time: Spring 2018


This is a seminar course. By reading and discussing important papers from computational social science, students will become familiar with core issues and techniques in the field. Students will also propose and implement individual research projects, which can potentially lead to publications.


Data collected through digital systems, such as online social networks, search engines, mobile phones, apps, etc., offer great opportunities for addressing important research questions about individual as well as collective human behavior. Whereas such issues had previously been studied primarily by social scientists, the sheer size of modern social data sets, as well as the fact that they are produced within computational systems, requires computational ways of thinking about, and processing, them.

The goal of this seminar is to acquaint students with some of the fundamental questions and techniques arising in the context of computational social science, e.g.,

-           network analysis of social systems,

-           machine learning and data mining for social systems,

-           text analysis and natural language processing of social phenomena,

-           large-scale social experiments,

-           drawing valid conclusions from 'found data' (a.k.a. observational studies),

-           integrated human-machine decision-making (incl. crowdsourcing),

-           algorithmic bias and accountability,

-           social information and communication dynamics (e.g., information diffusion),

-           ethics of computational research on human behavior.

We will explore the above topics by reading, discussing, and extending important papers from computational social science. Every week, we will focus on one paper. All students will write a short summary and review of the paper, and one student will lead the in-class discussion. Later in the semester, students will propose and implement individual projects, which can potentially lead to publications in workshops or conferences.

Beyond familiarizing themselves with research in the field, students will become better at assessing and critiquing scholarly work (by discussing and reviewing papers) and at identifying and implementing novel research questions (through the course project).

Learning prequisites: no formal prerequisites, but we expect students to have a basic understanding of statistics, probabilities, and machine learning

Learning Outcomes: summarize and critique scientific papers; identify, propose, and implement a novel research project in the context of the papers discussed in class


computational social science, social networks, text analysis, natural language processing, information dynamics, machine learning

In the programs

Reference week

      Exercise, TP
      Project, other


  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German