Purpose The purpose of this study is to examine the extent to which dynamic network analysis (DNA), text mining and natural language processing (NLP) are helpful research tools in identifying the key actors in a complex international crisis. The study uses these tools to identify the key actors in the Syrian crisis as a case study to validate the proposed algorithm. Design/methodology/approach To achieve its main purpose, the study uses a collection of three methodologies, namely, DNA, text mining and NLP. Findings The results of the analysis show four key actors in the Syrian crisis, namely, Russia, the USA, Turkey and China. The results also reveal changes in their powerful positions from 2012 to 2016, which matches the changes that occurred in the real world. The matching between the findings of the proposed algorithm and the real world events that happened in Syria validate our proposed algorithm and proves that the algorithm can be used in identifying the key actors in complex international crises. Originality/value The importance of the study lies in two main points. It proposes a new algorithm that mixes NLP, network extraction from textual unstructured data and DNA to understand and monitor changes occurring in a complex international crisis. It applies the proposed algorithm on the Syrian crisis as a case study to identify the key actors and hence validate the proposed algorithm.
The world is now in an age where the electronic media has drawn people from different cultures closer together into what is called a "Global Society". The flow of ideas, thoughts, and values has become much easier and more rabid than before. Tolerant ideas and values as well as hatred and violent thoughts can propagate easier in this new global society. While violent and terrorist ideas propagate quickly (as in case of ISIS), tolerant ideas and practices may fail to reach their target population efficiently due to the lack of an explicit diffusion strategy and/or the lack of an implementation strategy that acknowledge the social structure that binds together the members of the targeted community. The article assumes that we can create the basis for the emergence of a social context where tolerance values can be diffused by targeting social networks not social classes, age, religious groups, or institutions. This article outlines how social network analysis can be effective as: 1) a new framework of analysis that is more relevant for understanding, describing and dealing with community dynamics, 2) a tool for diffusing tolerant ideas (memes) within a targeted community, and 3) an identifier of the influential actors in the target community in order to optimize the diffusion of tolerant memes.
Purpose The purpose of this study is twofold; first, it aims to understand the underlying dynamics of the organizations behind the terrorist attacks, and second, to investigate the dynamics of terrorist organizations in relation to one another to detect whether there exist shared patterns of terror between different organizations. Design/methodology/approach To achieve this purpose, the researcher proposes a computational algorithm that extracts data from global terrorism database (GTD); calculates similarity indices between different terrorist groups; generates a network data file from the calculated indices; and apply network analysis techniques to the extracted data. The proposed algorithm includes applying SQL database codes for data extraction, building a tailored C# computer software to calculate similarity indices and generate similarity networks and using GEPHI software to visualize the generated network and calculate network metrics and measures. Findings Applying the proposed algorithm to Egypt, the results reveal different shared patterns of terror among different terrorist groups. This helps us in creating a terror landscape for terrorist groups playing in Egypt. Originality/value The importance of the study lies in that it proposes a new algorithm that combines network analysis with other data-manipulation techniques to generate a network of similar terror groups.
Purpose This paper aims at understanding the dynamics underlying toleration as a complex social phenomenon and its pattern on Facebook during the June 30th revolution in Egypt. Thanks to the huge advances in ICT, internet-mediated research (IMR) has become one of the most prominent research methodologies in social sciences. Discussions on social network sites cannot be neglected in studying the dynamics complex and emerging social phenomena such as changes in public opinion, culture, attitudes and virtues. Design/methodology/approach To fulfill this aim, the researchers used web content analysis as a method inside IMR paradigm to analyze the discussions on Tamarrod’s Facebook page in the period from June 30th to July 5th and to examine the emerging overall pattern of toleration. Findings The results show indications that toleration is inherent in the Egyptian culture, and that the Egyptian society still keeps its reputation as a highly tolerant society, even in crises periods where tensions are witnessed everywhere. Moreover, the results also show that the web content analysis process proposed in this study is highly reliable and valid. Originality/value The importance of the study lies in introducing a computational and empirical approach to analyze web content in a semi-automated way and proving its validity and reliability to study social phenomena such as toleration.
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