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.
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Acknowledgements
This work was supported in part by the Office of Naval Research (ONR) through a MINERVA N000141310835 on State Stability. Additional support for this project was provided by the center for Computational Analysis of Social and Organizational Systems (CASOS) at CMU. The views ond conclu- sions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research or the U.S. Government.
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Benigni, M., Joseph, K. & Carley, K.M. Mining online communities to inform strategic messaging: practical methods to identify community-level insights. Comput Math Organ Theory 24, 224–242 (2018). https://doi.org/10.1007/s10588-017-9255-3
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DOI: https://doi.org/10.1007/s10588-017-9255-3