Twitter has become an important tool for communication and marketing. Topic model algorithms meant to characterize the discourse of online conversations and identify relevant audiences do not perform well for this task, despite their widespread usage. This paper proposes an iterative topic model, Gamma Filtration, and a social network-based method, Simmelian Filtration, to amplify tweet-topic probability signal and reduce noise. We demonstrate the method on a novel data set collected of European Racially and Ethnically Motivated Violent Extremist (REMVE) networks on Twitter. We find that Simmelian Filtering is most successful at reducing noise as measured by perplexity. This improves our ability to detect and monitor core conversations of a community that is disseminating propaganda to increase online extremism.