|題名: ||南海緊張情勢：GDELT 時間序列數據之分析|
South China Sea Tensions : State Involvement and Prediction Using GDELT Event Data
Maritime territorial disputes
South China Sea
|上傳時間: ||2018-04-09 16:05:31 (UTC+8)|
Discussions of the South China Sea maritime territorial disputes are rife with assertions that certain state actors escalate regional tensions and that it is only a matter of time before provocations trigger armed conflict. However, these claims are based primarily on incomplete evidence, inaccurate comparisons with historical conflicts, and country or individual biases. This dissertation questions these common assertions and uses empirical evidence to assess their validity. Using time-series event data from the Global Database of Events, Language, and Tone (GDELT), it analyzes (1) the relationship between state involvement and South China Sea tensions and (2) which forecast models can most accurately predict South China Sea tensions based on data from earlier time periods.
For RQ1, the analyses reveal that the involvement of certain countries corresponds with significantly higher tensions in the South China Sea, that state involvement and tensions are correlated at various positive and negative lags of interest, and that these correlations go in both directions. These findings have important implications for policymakers and researchers in that they offer empirical evidence that confirms or refutes assertions suggesting that certain countries’ actions lead to escalation or deescalation. They also provide a solid foundation for future research, which could take specific countries as individual case studies to further investigate the relationships between state involvement and South China Sea tensions. Moreover, the results indicate that there may be even more interesting phenomena at play that merit attention in future research: evidence suggesting that certain countries may either contribute to lower tensions or avoid becoming involved when there are heightened tensions, and evidence that some countries may not be contributing to but instead reacting to tensions and volatility in the South China Sea.
For RQ2, two of the four forecast models perform better than the four benchmark models using both datasets. These findings also have important implications for policy and research. As governments become increasingly interested in using continuously updated global databases to facilitate policy-making, the results suggest that empirical data can help to inform conclusions about trends of escalation and deescalation in the South China Sea and be used to make relevant predictions. As a first cut at the data and a pioneering approach to analyzing South China Sea tensions, the analyses and findings of this dissertation represent a significant contribution to knowledge and a foundation for future research using time-series event data to understand the relationship between state involvement and tensions and the extent to which tensions can be forecasted in the South China Sea and around the world.
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