Diving in the Digital Public Space: From individual (behavioral) digital traces to collective social and political dynamics

Graduate Program (& Advanced Certificate) Status

Course Level: 
Course Open to: 
Students on-site
Academic Year: 
US Credits: 
ECTS Credits: 
Course Code: 
DNDS 6007
Course Description: 

This is a shared course organized by the Department of Network and Data Science - CEU Vienna and the Médialab - Sciences Po Paris as part of the CIVICA European University program. The course will be taught online by two instructors for students at both universities.

Short Bio of the Instructors

Márton Karsai, PhD, Habil., Associate professor in the Department of Network and Data Science at the Central European University, director of the PhD Program in Network Science. He is a researcher of the Rényi Institute of Mathematics, fellow of the ISI Foundation and scientific advisor of the UNICEF Office of Innovation. He is a physicist trained network scientist with research interest in human dynamics, computational social science, and data science, especially focusing on systems with heterogeneous dynamics, spatial and temporal networks, socioeconomic systems and social and biological contagion phenomena.

Jean-Philippe Cointet is working at the Sciences Po médialab, where he designs innovative computational sociology methods. He is specialized in text analysis, and is working on various kinds of corpora and sources questioning their socio-political dynamics. His research areas are diverse, ranging from social media analysis (Facebook public posts and comments) to science of science (data turn in oncology) or political processes mapping (political discourses, international negotiations) and frame analysis (press coverage of migration processes). He also participates in developing  the CorText platform. He holds a PhD in Complex Systems and was trained as an engineer at Ecole Polytechnique. He is also affiliated with the research center INCITE, from Columbia University.

The course invites students to collect, model and visualize data from social media platforms. Data built from individual behaviors of users on Twitter, Facebook or Youtube are playing an increasing role in marketing, political targeting or even epidemic spreading forecasting. In this joint course between CEU and Sciences Po, we teach students the basics of data science applied to social media platforms and call for imagining alternative use of traditional AI powered data-analysis algorithms.

In this class, participants will get their hands on data and code and put to test existing state-of-the-art data science methods onto their own data to investigate a research question related to social and political dynamics at large: linguistic trends, social mobilizations, systematic discriminations, etc.

The pedagogical format is strongly oriented toward a workshop-style class. Typically, a short theoretical introduction will first be given. A discussion of the readings will follow before the class turns into lecturing mode, that will include more practical parts. One hands-on session (week 5) wherein students will practice data and algorithms coding is planned. The scheduling of the course slightly deviates from the usual format so that it can respect the academic calendars of both universities.

The classes will follow the below schedule: 


This is a joint class between CEU and Sciences Po. As such, classes will take place on Zoom and collective work will also need to be organized remotely.

Learning Outcomes: 

The objectives of the class are threefold:

(i) learn and practice large data collection, manipulation and critical text and network analysis tools,

(ii) create your own research plan to investigate some original dataset or question in a collective project,

(iii) develop reflexivity and gain critical distance with the data-science & AI literature (and promises) through a series of readings.


There will be two main assessments during the semester. The most important one (60% of the grade) is a collective project that will be presented during the last session of the semester. In this original project, students will be required to identify a research question, collect an online dataset and design an experimental plan to analyze it accordingly. The final delivery will take the form of a notebook published on github summarizing the data workflow and discussing interpretations. An individual take-home paper will also be graded (30%). Participation during the class will also be evaluated (10% of the final grade).


  • In-class presence: 2 hours a week (except for two special sessions that will last 3 hours) / 20 hours a semester
  • Online learning activities: 20 minutes a week / 3 hours a semester
  • Reading and preparation for class: 45 minutes a week / 7 hours a semester
  • Research and preparation for group work: 2 hours a week / 18 hours a semester
  • Research and writing for individual assessment: 20 minutes a week / 3 hours a semester

Basic Python skills are required (building custom functions, importing and using libraries, manipulating lists and dictionaries, a first experience with pandas is appreciated). We are also expecting students to be curious about computer science, data & algorithms and their social and political roles.