Racism, nationalism and right-wing extremism online: The Austrian Presidential Election 2016 on Facebook

Thee 2016 Austrian presidential election saw a run-o between the Green party candidate Alexander Van der Bellen and the Freedom Party of Austria’s (FPÖ) far-right candidate Norbert Hofer. This paper asks: How did voters of Hofer express their support on Facebook? It presents the results of a qualitative ideology analysis of 6755 comments about the presidential election posted on the Facebook pages of FPÖ leader Heinz-Christian Strache and FPÖ candidate Hofer. The results reveal insights into the contemporary political role of the online leadership ideology, online nationalism, new racism online, the friend/enemy-scheme online, and online militancy. Right-wing extremism 2.0 is a complex problem that stands in the context of contemporary crises and demagoguery.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.