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Mining online communities to inform strategic messaging: practical methods to identify community-level insights

Matthew Benigni (), Kenneth Joseph and Kathleen M. Carley
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Matthew Benigni: Carnegie Mellon University
Kenneth Joseph: Northeastern University
Kathleen M. Carley: Carnegie Mellon University

Computational and Mathematical Organization Theory, 2018, vol. 24, issue 2, No 4, 224-242

Abstract: Abstract The ability of OSNs to propagate civil unrest has been powerfully observed through the rise of the ISIS and the ongoing conflict in Crimea. As a result, the ability to understand and in some cases mitigate the effects of user communities promoting civil unrest online has become an important area of research. Although methods to detect large online extremist communities have emerged in literature, the ability to summarize community content in meaningful ways remains an open research question. We introduce novel applications of the following methods: ideological user clustering with bipartite spectral graph partitioning, narrative mining with hash tag co-occurrence graph clustering, and identifying radicalization with directed URL sharing networks. In each case we describe how the method can be applied to social media. We subsequently apply them to online Twitter communities interested in the Syrian revolution and ongoing Crimean conflict.

Keywords: Social networks; Online extremist community; Online extremism; Social media; Twitter; Hashtags; Terrorism; ISIS; Euromaidan (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10588-017-9255-3

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