Сравнение на методи

	Event Data Analysis of Conflict	Alliance Network Analysis
Област	International Relations	International Relations
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1994	2012
Създател	Philip Schrodt (KEDS/TABARI); ICEWS team (Boschee et al.)	Skyler Cranmer, Bruce Desmarais & Elizabeth Menninga
Тип	Automated extraction of structured political events from news text	Network analysis and inferential network modeling of interstate alliances
Основополагащ източник	Schrodt, P. A., Davis, S. G., & Weddle, J. L. (1994). Political science: KEDS — A program for the machine coding of event data. Social Science Computer Review, 12(4), 561–588. See also Gerner, Schrodt et al. (1994), Machine coding of event data using regional and international sources, International Studies Quarterly, 38(1), 91–119.	Cranmer, S. J., Desmarais, B. A., & Menninga, E. J. (2012). Complex dependencies in the alliance network. Conflict Management and Peace Science, 29(3), 279–313.
Други названия	Political Event Data, Machine-Coded Conflict Event Data, Conflict Event Extraction, Who-Did-What-to-Whom Event Coding	International Alliance Networks, Alliance Portfolio Network Analysis, Network Models of Alliance Formation, Interstate Alliance Graph Analysis
Свързани	4	3
Резюме	Event data analysis is the automated extraction of structured records of political interactions — who did what to whom, when, and where — from large volumes of news text, for the quantitative study of conflict and cooperation. Pioneered for machine coding by Philip Schrodt with the KEDS and TABARI systems and scaled in projects such as ICEWS and GDELT, it turns unstructured reporting into dated actor-action-target triples coded to an ontology like CAMEO, which can then be aggregated into time series of interstate or intrastate hostility.	Alliance network analysis studies international alliances as a graph of states linked by formal security commitments, and models how that network forms and evolves. Rather than treating each alliance dyad as independent, it uses network science and inferential models such as the exponential random graph model (ERGM) — applied to alliance data by Cranmer, Desmarais, and Menninga (2012) — to capture the complex dependencies, such as a state's tendency to ally with its allies' allies, that ordinary dyadic regression assumes away.