@InProceedings{hartung-EtAl:2017:WASSA2017,
  author    = {Hartung, Matthias  and  Klinger, Roman  and  Schmidtke, Franziska  and  Vogel, Lars},
  title     = {Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {24--33},
  abstract  = {Social media are used by an increasing number of political actors. A
	small subset of these is interested in pursuing extremist 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 F$\_1$ 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.},
  url       = {http://www.aclweb.org/anthology/W17-5204}
}