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.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10588-017-9255-3/MediaObjects/10588_2017_9255_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10588-017-9255-3/MediaObjects/10588_2017_9255_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10588-017-9255-3/MediaObjects/10588_2017_9255_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10588-017-9255-3/MediaObjects/10588_2017_9255_Fig4_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10588-017-9255-3/MediaObjects/10588_2017_9255_Fig5_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10588-017-9255-3/MediaObjects/10588_2017_9255_Fig6_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10588-017-9255-3/MediaObjects/10588_2017_9255_Fig7_HTML.gif)
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Barbera P (2014) Tweeting the revolution: social media use and the #euromaidan protests | huffington post
Benigni M, Carley KM (2016) From tweets to intelligence: understanding the Islamic Jihad supporting community on Twitter. In social computing, behavioral-cultural modeling, and prediction, page to appear. Springer, New York
Benigni M, Joseph K, Carley KM (2017) Online extremism and the communities that sustain it: Detecting the isis supporting community on twitter. Under Revision to PLOS ONE
Berger JM, Morgan J (2015) The isis twitter census: defining and describing the population of isis supporters on twitter. The Brookings Project on US Relations with the Islamic World 3(20)
Berger JM (2015) Tailored online interventions: the Islamic state’s recruitment strategy. Combating Terrorism Center Sentinel
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech 2008(10):P10008
Callimachi R (2015) ISIS and the Lonely Young American. The New York Times
Carley KM (2006) A dynamic network approach to the assessment of terrorist groups and the impact of alternative courses of action. Technical report
Chang HC (2010) A new perspective on twitter hashtag use: Diffusion of innovation theory. Proc Am Soc Inf Sci Technol 47(1):1–4
DeMasi O, Mason D, Ma J (2016) Understanding communities via hashtag engagement: a clustering based approach. In: Tenth International AAAI Conference on Web and Social Media
Dewey T, Kaden J, Marks M, Matsushima S, Zhu B (2012) The impact of social media on social unrest in the Arab Spring. Int Policy Prog
Dhillon IS (2001) Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 269–274
Dozier K (2016) Anti-ISIS-Propaganda Czars Ninja war plan: we were never here, March 2016
Fortunato S (2010) Community detection in graphs. Physics Rep 486(3):75–174
Glasgow K, Fink C (2013) Hashtag lifespan and social networks during the london riots. In: Greenberg AM, Kennedy WG, Bos ND (eds) Social computing, behavioral-cultural modeling and prediction. Springer, Berlin, pp 311–320. doi:10.1007/978-3-642-37210-0_34
Goodman LA (1961) Snowball sampling. Ann Math Stat 32(1):148–170
Hamilton WL, Clark K, Leskovec J, Jurafsky D (2016) Inducing domain-specific sentiment lexicons from unlabeled corpora
Herrick D (2016) The social side of cyber power? social media and cyber operations. In: Cyber Conflict (CyCon), 2016 8th International Conference on, NATO CCD COE, pp 99–111
Howard PN, Parks MR (2012) Social media and political change: capacity, constraint, and consequence. J Commun 62(2):359–362
Hussain MM, Howard PN (2013) What best explains successful protest cascades? ICTs and the fuzzy causes of the Arab Spring. Int Stud Rev 15(1):48–66
Jensen DN (2016) Lennart meri and the ’new normal’ | huffington post
Johnson NF, Zheng M, Vorobyeva Y, Gabriel A, Qi H, Velasquez N, Manrique P, Johnson D, Restrepo E, Song C, Wuchty S (2016) online ecology of adversarial aggregates: ISIS and beyond. Science 352(6292):1459–1463
Juris JS (2012) Reflections on #occupy everywhere: social media, public space, and emerging logics of aggregation. Am Ethnol 39(2):259–279
Lloyd Stuart (1982) Least squares quantization in pcm. IEEE Trans Inf Theory 28(2):129–137
Loader BD, Mercea D (2011) Networking democracy? Inf Commun Soc 14:757–769
Lotan G, Graeff E, Ananny M, Gaffney D, Pearce I, Boyd D (2011) The revolutions were tweeted: information flows during the 2011 Tunisian and Egyptian revolutions. Int J Commun 5:1375–1405
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1, University of California Press, pp 281–297
Nanabhay M, Farmanfarmaian R (2011) From spectacle to spectacular: how physical space, social media and mainstream broadcast amplified the public sphere in Egypt’s Revolution’. J N Afr Stud 16(4):573–603
Papadopoulos S, Kompatsiaris Y, Vakali A, Spyridonos P (2012) Community detection in social media. Data Min Knowl Discov 24(3):515–554
Peel L, Larremore DB, Clauset A (2016) The ground truth about metadata and community detection in networks
Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. Proc Emprical Methods Nat Lang Process 12:1532–1543
REST Twitter (2016) API
Roxburgh G (2016) Ukraine wins the 2016 eurovision song contest | news | eurovision song contest
Sakaki T, Okazaki M, Matsuo Y (2016) Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web WWW ’10, ACM, pp 851–860
Settles B (2009) Active learning literature survey. Computer Sciences Technical Report 1648. University of Wisconsin, Madison
Shamanska A (2016) Hackers in Ukraine deface separatist websites to mark victory day. Accessed 23 Oct 2016
Starbird K, Palen L (2012) (How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising. In: Proceedings of the acm 2012 conference on computer supported cooperative work, ACM, 2012, pp 7–16
Steinbach M, Karypis G, Kumar V, et al. (2000) A comparison of document clustering techniques. In: KDD workshop on text mining, vol 400, Boston, pp 525–526
Szostek J (2014) The media battles of Ukraine’s EuroMaidan. Digital Icons 11:1–19
Tang L, Wang X, Liu H (2009) Uncoverning groups via heterogeneous interaction analysis. In: Ninth IEEE International Conference on Data Mining, ICDM’09, IEEE, pp 503–512
Tufekci Z (2014) Big questions for social media big data: representativeness, validity and other methodological pitfalls. In: ICWSM ’14: Proceedings of the 8th international AAAI conference on weblogs and social media
UNIAN (2016) Rada adopts bill allowing lutsenko to be nominated prosecutor general. Accessed 20 Oct 2016
Veilleux-Lepage Y (2015) Paradigmatic Shifts in Jihadism in Cyberspace: the emerging role of unaffiliated sympathizers in the Islamic State’s social media strategy. J Terror Res
Veilleux-Lepage Y (2014) Retweeting the Caliphate: the role of soft-sympathizers in the Islamic state’s social media strategy. In: 2014 6th International Terrorism and Transnational Crime Conference, March 2014
Wagstaff K, Cardie C, Rogers S , Schrdl S, et al. (2001)Constrained k-means clustering with background knowledge. In: ICML, vol 1, pp 577–584
Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge
Weber I, Garimella VRK, Teka A (2013) Political hashtag trends. In: Serdyukov P, Kuznetsov OS, Kamps J, Agichtein E, Segalovich I, Yilmaz E (eds) Advances in information retrieval. Springer, Berlin, pp 857–860. doi:10.1007/978-3-642-36973-5_102
Wei W, Joseph K, Liu H, Carley KM (2015) The fragility of twitter social networks against suspended users. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp 9–16
Wolfsfeld G, Segev E, Sheafer T (2013) Social media and the arab spring politics comes first. Int J Press 18(2):115–137
Wood P (2016) IS conflict: counting the civilian cost of US-led air strikes
Zweig KA, Kaufmann M (2011) A systematic approach to the one-mode projection of bipartite graphs. Soc Netw Anal Min 1(3):187–218
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10588-017-9255-3