Sammenlign metoder

	Event Data Analysis of Conflict	Event Data Analysis
Fagområde	International Relations	Political Science
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1994	—
Ophavsperson	Philip Schrodt (KEDS/TABARI); ICEWS team (Boschee et al.)	Conflict-studies and computational-social-science traditions (McClelland, Schrodt, King)
Type	Automated extraction of structured political events from news text	Automated coding and analysis of who-did-what-to-whom event records
Oprindelig kilde	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.	Schrodt, P. A. (2012). Precedents, Progress, and Prospects in Political Event Data. International Interactions, 38(4), 546–569.
Aliasser	Political Event Data, Machine-Coded Conflict Event Data, Conflict Event Extraction, Who-Did-What-to-Whom Event Coding	Event data coding, Political event data, Conflict event data, CAMEO event coding
Relaterede	4	3
Resumé	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.	Event data analysis converts streams of news reports into structured records of political interactions — who did what to whom, when — and aggregates them into time series of cooperation and conflict between actors. Each event is coded as a source actor, an action type drawn from an ontology such as CAMEO, a target actor, and a date. Modern systems extract these events automatically from millions of news stories, enabling near-real-time measurement of interstate and intrastate behavior for forecasting and analysis.