Umut Nefta Kanilmaz


I am a researcher and PhD candidate at the department of Geoinformatics Z_GIS at the Paris-Lodron-University Salzburg. Previously, I worked as a software engineer with focus on scalable big data pipelines for machine learning applications. Prior to the COVID pandemic, I liked to organize Python Meetups in Hamburg, nowadays I use my free time to go bikepacking and explore local beer gardens in Salzburg.

Understanding the evolution and spread of conspiracy theories using Twitter user network data Cancelled

Anglický jazyk

Umut Nefta Kanilmaz

In the beginning of 2020, the so-called "Querdenken" movement formed: a protest movement that critized government regulations concerning the containment of the COVID-19 pandemic. This movement reached popularity in Austria and radicalized as more and more right-wing extremist players appeared.

I want to show you how I study the spatio-temporal evolution of the Querdenken movement in Austria by analyzing the social network of Twitter users. For this, I will briefly explain how social networks can be modeled with basic graph theory. This approach then allows to determine central players and learn about potential communities within the movement. Combining the user information with the geographic view, we can understand how the movement organized and spread across Central Europe. I will lay a particular focus on how I retrieve, store and handle the data needed for my analysis and talk about the Python libraries and database tools I considered and used.