পদ্ধতির তুলনা করুন

	ACLED Event Analysis	Event Data Analysis of Conflict
ক্ষেত্র	International Relations	International Relations
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2010	1994
প্রবর্তক	Clionadh Raleigh, Andrew Linke, Håvard Hegre & Joakim Karlsen	Philip Schrodt (KEDS/TABARI); ICEWS team (Boschee et al.)
ধরন	Disaggregated coding and analysis of political-violence events	Automated extraction of structured political events from news text
মৌলিক উৎস	Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing ACLED: An armed conflict location and event dataset. Journal of Peace Research, 47(5), 651–660.	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.
অপর নাম	ACLED Analysis, Armed Conflict Location and Event Data, Political Violence Event Analysis, Disaggregated Conflict Event Analysis	Political Event Data, Machine-Coded Conflict Event Data, Conflict Event Extraction, Who-Did-What-to-Whom Event Coding
সম্পর্কিত	3	4
সারসংক্ষেপ	ACLED event analysis is the disaggregated study of political violence and protest using the Armed Conflict Location and Event Data project, introduced by Raleigh, Linke, Hegre, and Karlsen (2010). ACLED codes individual events — battles, violence against civilians, riots, protests, explosions and remote violence, and strategic developments — with their date, location, actors, and any fatalities, updated on a near-weekly basis. Its fine granularity and timeliness make it a workhorse for mapping, monitoring, and modeling where, when, and by whom political violence occurs.	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.