Abstract
Social media are used by an increasing number of political actors. A small subset of these is interested in pursuing extrem- ist motives such as mobilization, recruit- ing or radicalization activities. In order to counteract these trends, online providers and state institutions reinforce their mon- itoring efforts, mostly relying on manual workflows. We propose a machine learn- ing approach to support manual attempts towards identifying right-wing extremist content in German Twitter profiles. Based on a fine-grained conceptualization of right- wing extremism, we frame the task as rank- ing each individual profile on a continuum spanning different degrees of right-wing extremism, based on a nearest neighbour approach. A quantitative evaluation reveals that our ranking model yields robust per- formance (up to 0.81 F1 score) when being used for predicting discrete class labels. At the same time, the model provides plausi- ble continuous ranking scores for a small sample of borderline cases at the division of right-wing extremism and New Right political movements.
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