TY  - BOOK
AB  - 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, recruiting or radicalization activities. In order to counteract these trends, online providers and state institutions reinforce their monitoring efforts, mostly relying on manual workflows. We propose a machine learning 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 ranking 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 performance (up to 0.81 F1 score) when being used for predicting discrete class labels. At the same time, the model provides plausible continuous ranking scores for a small sample of borderline cases at the division of right-wing extremism and New Right political movements.
DA  - 2017
DO  - 10.18653/v1/W17-5204
KW  - right wing extremism
KW  - social media monitoring
KW  - machine learning
KW  - ranking
LA  - eng
PY  - 2017
TI  - Ranking right-wing extremist social media profiles by similarity to democratic and extremist groups
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29127570
Y2  - 2024-11-21T16:33:47
ER  -