Conflict and Cooperation in an Actors' Network of Chechnya Based on Event Data

The conflict in Chechnya is characterized by a high degree of complexity. To capture this complexity, the authors produced an actors' network analysis drawing on one of the largest event databases today. The aim was to identify the main actors involved in the conflict, track down the most impor...

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Bibliographic Details
Published in:The Journal of conflict resolution 2006-04, Vol.50 (2), p.159-175
Main Authors: Hämmerli, August, Gattiker, Regula, Weyermann, Reto
Format: Article
Language:English
Subjects:
Armed conflict
Armed forces
Assessment Centers (Personnel)
Centre-periphery relations
Chechnya
Civil war
Civilians
Cluster analysis
Conflict resolution
Cooperation
Deportation
Government (Administrative Body)
Information Sources
International relations
Military Personnel
Multivariate Analysis
Network Analysis
Pattern Recognition
Political conflict
Political networks
Politics
Population Distribution
Rebels
Russia
Russian Federation
Russian government
Separatism
Social network analysis
Social Networks
Sociology
Studies
Topology
Transcaucasia
Warnings
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Summary:The conflict in Chechnya is characterized by a high degree of complexity. To capture this complexity, the authors produced an actors' network analysis drawing on one of the largest event databases today. The aim was to identify the main actors involved in the conflict, track down the most important conflictive and co-operative ties between actors, and compare the quality and intensity of interactions among actor groups. In total, 2,818 events reported between 2002 and 2005 were assigned forty-four actors categories. Measures of centrality and power and a combination of principal component and cluster analysis were used to produce graphs of the network. The authors found civilians to be the most important actors, closely followed by the Russian military, the Chechen fighters, and the Chechen government. The results are discussed in light of the current situation in Chechnya with reference to chances and limitations of the network approach with event data.
ISSN:0022-0027
1552-8766
DOI:10.1177/0022002705284826