Generative Adversarial Network Models for electoral behaviours studies

Generative Adversarial Network Models for electoral behaviours studies

Survey experiments in studies of political and electoral behavior using profiles of potential candidates are standard practice in political science. With this type of experiments, the researchers' goal is to test which characteristics voters value most in political candidates. One of the main challenges, however, is to generate realistic profiles to be tested. One of the important components of these profiles are the faces of candidates. A potential option is to use faces of real candidates, but this involves complex legal and ethical issues. Another option is to hire models to represent candidates, but this can be expensive and time-consuming.

In this project, we suggest a third option namely to use Generative Adversarial Models (GANs) to generate artificial, but realistic images of faces. The generated images will be used as survey images of real candidates in Brazilian elections


The result of this thesis has the potential to be used in ongoing and future research projects. Moreover, the student can explore the use of the developed outcome to other research topics in applied social sciences, such as racial/ethnic discrimination, and cognitive bias.

Learning outcome

  • Work on a project with practical use in different fields
  • Collaborate with researchers in an interdisciplinary way
  • Knowledge that can be easily applied to different purposes


  • Programming
  • Motivation
  • Interest in social sciences


Collaboration partners

  • OsloMet

Associated contacts

Michael Riegler

ProfessorChief Research Scientist/Research Professor

Hugo Hammer

Adjunct Chief Research Scientist