\begin{figure}
\centering
\includegraphics{figure_2.pdf}
\caption{Continuous ranking of differential profiles (cf. Section 4.1). Position on the scale indicates the ranking score as given in Equation (3), based on optimal parameters $k=4$ and $\ell=5$. The marked data point is assigned different categories by ranking and classification models (cf. discussion in Section 4.2.2).}
\label{figure_2}
\end{figure}

\begin{table}
\centering
\caption{Results of analyzing the impact of individual feature groups in the ranking model when being used in isolation (on test set)}
\label{table_2}
\begin{tabular}{lccc ccc ccc}
 & \multicolumn{3}{c}{Lexical Features} & \multicolumn{3}{c}{Emotion Features} & \multicolumn{3}{c}{Pattern Features} \\
 & P & R & F$_1$ & P & R & F$_1$ & P & R & F$_1$ \\
discrete decoding unbalanced ($k=4$, $\ell=5$) & 0.79 & 0.85 & 0.81 & 0.38 & 0.62 & 0.47 & 1.00 & 0.08 & 0.14 \\
discrete decoding balanced ($k=10$, $\ell=10$) & 0.80 & 0.62 & 0.70 & 0.20 & 0.38 & 0.26 & 0.00 & 0.00 & 0.00 \\
discrete decoding balanced ($k=1$, $\ell=1$) & 0.69 & 0.69 & 0.69 & 0.48 & 0.85 & 0.61 & 0.63 & 0.38 & 0.48 \\
\end{tabular}
\end{table}

First, higher degrees of emotion in language use are clearly associated with category R profiles. Individual emotions most strongly associated with one of the categories are surprise, trust and disgust (for right-wing extremists), and love and sadness (for non-extremist users). Second, the most highly weighted pattern features for category R are GEGEN\_Masseneinwanderung (``mass immigration''), UNSER\_Politiker (``politicians''), UNSER\_Fahne (``banner''), GEGEN\_Syrien (``Syria'') and GEGEN\_Merkel, whereas UNSER\_Land (``country''), GEGEN\_Rechts (``Right-wing''), GEGEN\_Gebietsreform (``territorial reform''), PRO\_Aufkl\"arung (``information'') and UNSER\_Jugendkandidat*innen (``youth contestants'') are the most indicative patterns of category N.

\section{Conclusions and Outlook}

In this paper, we have presented a ranking model to identify Twitter profiles which display traits or attitudes of right-wing extremism. Our work is motivated by the goal of supporting human experts in their monitoring activities which are currently carried out purely manually.

Similarly to standard nearest-neighbour classification approaches, the model is based on estimating the relative proximity of an unseen profile to a limited number of manually annotated groups of seed profiles in high-dimensional vector space. We apply this model in the two settings of discrete decoding and continuous ranking. Our evaluation shows a significant advantage of the ranking model over a binary classification approach \cite{Hartung_2017}. At the same time, the ranking model is found to deliver plausible predictions for a sample of borderline cases which specifically address actors from New Right political movements in Germany, whose categorization as right-wing extremists is currently debated in the social sciences (cf. \cite{Zick_2016}).

The latter finding clearly deserves a more thorough investigation based on a larger sample of cases, which we would like to address in future work. Additionally, we aim at developing this method further into a learning-to-rank approach in order to enable the comparison of profiles based on weighted properties. Finally, we propose the development of features that are based on deeper methods of natural language analysis in order to be able to address more fine-grained aspects in the conceptualization of right-wing extremism.