Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining communities and their relationships in blogs: A study of online hate groups
International Journal of Human-Computer Studies
Detecting Structural Changes and Command Hierarchies in Dynamic Social Networks
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
Inferring relevant social networks from interpersonal communication
Proceedings of the 19th international conference on World wide web
Tracking the Evolution of Communities in Dynamic Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Recipe recommendation using ingredient networks
Proceedings of the 3rd Annual ACM Web Science Conference
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Recent years have seen increased interest in the online presence of extreme right groups. Although originally composed of dedicated websites, the online extreme right milieu now spans multiple networks, including popular social media platforms such as Twitter, Facebook and YouTube. Ideally therefore, any contemporary analysis of online extreme right activity requires the consideration of multiple data sources, rather than being restricted to a single platform. We investigate the potential for Twitter to act as one possible gateway to communities within the wider online network of the extreme right, given its facility for the dissemination of content. A strategy for representing heterogeneous network data with a single homogeneous network for the purpose of community detection is presented, where these inherently dynamic communities are tracked over time. We use this strategy to discover and analyze persistent English and German language extreme right communities.