Identifying key actors in an international crisis using dynamic network analysis: Syrian crisis case study
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...
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| Format: | Article |
| Language: | English |
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Emerald Publishing
2019-10-01
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| Series: | Journal of Humanities and Applied Social Sciences |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/JHASS-08-2019-0022/full/pdf?title=identifying-key-actors-in-an-international-crisis-using-dynamic-network-analysis-syrian-crisis-case-study |
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| author | Amira S.N. Tawadros Sally Soliman |
| author_facet | Amira S.N. Tawadros Sally Soliman |
| author_sort | Amira S.N. Tawadros |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:33:56Z |
| format | Article |
| id | doaj.art-099c48c775c9452a9da6a0ff40844586 |
| institution | Directory Open Access Journal |
| issn | 2632-279X |
| language | English |
| last_indexed | 2024-04-11T20:33:56Z |
| publishDate | 2019-10-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Journal of Humanities and Applied Social Sciences |
| spelling | doaj.art-099c48c775c9452a9da6a0ff408445862022-12-22T04:04:25ZengEmerald PublishingJournal of Humanities and Applied Social Sciences2632-279X2019-10-011213214510.1108/JHASS-08-2019-0022635007Identifying key actors in an international crisis using dynamic network analysis: Syrian crisis case studyAmira S.N. Tawadros0Sally Soliman1Department of SocioComputing, Faculty of Economics and Political Science, Cairo University, Giza, EgyptDepartment of SocioComputing, Faculty of Economics and Political Science, Cairo University, Giza, EgyptPurpose – 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.https://www.emerald.com/insight/content/doi/10.1108/JHASS-08-2019-0022/full/pdf?title=identifying-key-actors-in-an-international-crisis-using-dynamic-network-analysis-syrian-crisis-case-studydynamic network analysisnetwork analysisnatural language processingtext analysistext miningsyrian crisis |
| spellingShingle | Amira S.N. Tawadros Sally Soliman Identifying key actors in an international crisis using dynamic network analysis: Syrian crisis case study Journal of Humanities and Applied Social Sciences dynamic network analysis network analysis natural language processing text analysis text mining syrian crisis |
| title | Identifying key actors in an international crisis using dynamic network analysis: Syrian crisis case study |
| title_full | Identifying key actors in an international crisis using dynamic network analysis: Syrian crisis case study |
| title_fullStr | Identifying key actors in an international crisis using dynamic network analysis: Syrian crisis case study |
| title_full_unstemmed | Identifying key actors in an international crisis using dynamic network analysis: Syrian crisis case study |
| title_short | Identifying key actors in an international crisis using dynamic network analysis: Syrian crisis case study |
| title_sort | identifying key actors in an international crisis using dynamic network analysis syrian crisis case study |
| topic | dynamic network analysis network analysis natural language processing text analysis text mining syrian crisis |
| url | https://www.emerald.com/insight/content/doi/10.1108/JHASS-08-2019-0022/full/pdf?title=identifying-key-actors-in-an-international-crisis-using-dynamic-network-analysis-syrian-crisis-case-study |
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