Vertaile menetelmiä

	Sisällönanalyysi – Tekstin ja median systemaattinen koodaus	Event Data Analysis of Conflict
Tieteenala	Laadulliset menetelmät	International Relations
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	Systematised through Krippendorff's methodology work; 4th edition 2018	1994
Kehittäjä	Klaus Krippendorff (systematic formulation); roots in early 20th-century communications research	Philip Schrodt (KEDS/TABARI); ICEWS team (Boschee et al.)
Tyyppi	Qualitative / mixed-method research technique	Automated extraction of structured political events from news text
Alkuperäislähde	Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage. ISBN: 978-1506395661	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.
Rinnakkaisnimet	İçerik Analizi, systematic content coding, quantitative content analysis	Political Event Data, Machine-Coded Conflict Event Data, Conflict Event Extraction, Who-Did-What-to-Whom Event Coding
Liittyvät	5	4
Tiivistelmä	Content analysis is a systematic research technique for reducing text, visual, or media material into coded categories so that patterns can be counted, compared, and interpreted. Formalised by Klaus Krippendorff in his widely cited methodology textbook (latest edition 2018), the method sits at the boundary of qualitative and quantitative inquiry: it imposes structured, replicable coding on inherently meaning-laden material.	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.