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In the modern conflicts we often identify our enemies as terrorists with or with no enough reasoning, basing on political interest. In such situation it is very difficult to collect and to accumulate direct sociocultural and socio-psychological data, which we need to understand conflict behavior and nature of the conflict. At the same time, modern communicative tools, such as the social networks, offer number of ways to collect such information. So, the task of extraction, analysis and interpretation of sociocultural and socio-psychological information from social network data should be formulated, and its decision could be demonstrated for the separate case.
Changing nature of modern conflicts (Kaldor, 2013) requires novel approaches to its analysis and decision making (Duffield, 2014). New approaches require new data and algorithms for its processing. If tradition conflicts were driven by geo-political, economic or ideological interest, modern conflicts are the wars of identities, which have the internal, behaviorist reasons and drivers (Münkler, 2005). Because the rise of identity politics is associated with new communications technologies (Kaldor, 2013), the analysis of identity associated conflicts should include analysis of data from communicative networks.
Therefore, in the modern world, the terroristic threat and conflicts intensity are closely connected with the structure of information, the evolution of methods of information expansion and ways of its dissemination. Our decisions should be based on understanding of socio-psychological and sociocultural phenomena and processes, which influent to security (Bjørgo, 2004). Narrative analysis – in the most common sense of the narrative term, as a spoken or written account of connected events - is one of the useful ways of such understanding.
Despite existing volume of “stories of terrorists”, we still need material to understand the nature of current threats. Imperfectness of data sources is the reason of lack of information (Van der Hulst, 2009).
For example, stories of arrested terrorists are different from their real stories, like as police record is different than a detective novel. Arrested terrorist considers himself already dead, and this position influents to all components of narrative and deforming the analysis results.
Stories from “repentant terrorists” do not contain the necessary information, because the repentance act requires changing the worldview and ideology. So, we do not obtain direct information on cognitive and reflective instruments of terrorist. Therefore, we need “in vivo experiments”.
Usually, available in media stories from militants are essentially censored: it remains no more than 30-40% of original volume (real story told by a real person), 20-40% of the original lexicon is extracted, and in 10-20% of stories the structure of the narrative is changed (Rowland, 1989, Green, Strange, & Brock, 2003, Metsola, 2001). Therefore, we need stories from “first hands”.
This is a big, time-consuming and difficult task. Especially, if it is necessary to compare the materials from different countries, language and cultural communities (Presser & Sandberg, 2015; Joosse, Bucerius, & Thompson, 2015). So, the multisource data including social network and other big data resources should be involved into consideration. Processing this kind of data in security context requires the development of algorithms of big data analysis, but all available and accessible data should be identified and collected (Chen et al., 2004).
Comparison of IS (The Islamic State of Iraq and the Levant) and L/DPR (Lugansk and Donetsk People’s Republics) makes a sense and has reasons. The “official” history of the IS and L/DPR began in 2013-2014, they have approximately the same military potential of 30,000 fighters, more than half of which are foreign citizens, the methods and instruments of their military operations have signs of terrorist activity (Benmelech, Klor, 2016, Weiss, Hassan, 2016, Nowrasteh, 2016, Kostyuchenko, et al., 2017).