方法对比

	Event Data Analysis	Wordfish Scaling
领域	Political Science	Political Science
方法族	Process / pipeline	Latent structure
起源年份	—	2008
提出者	Conflict-studies and computational-social-science traditions (McClelland, Schrodt, King)	Jonathan Slapin and Sven-Oliver Proksch
类型	Automated coding and analysis of who-did-what-to-whom event records	Unsupervised latent-position model for word-count data
开创性文献	Schrodt, P. A. (2012). Precedents, Progress, and Prospects in Political Event Data. International Interactions, 38(4), 546–569.	Slapin, J. B., & Proksch, S.-O. (2008). A Scaling Model for Estimating Time-Series Party Positions from Texts. American Journal of Political Science, 52(3), 705–722.
别名	Event data coding, Political event data, Conflict event data, CAMEO event coding	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation
相关	3	4
摘要	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.	Wordfish scaling is an unsupervised text-as-data method that estimates a single latent position for each political document — a party manifesto, a legislative speech, a press release — directly from its word frequencies, without any reference texts or hand coding. Introduced by Slapin and Proksch in 2008, it models word counts as draws from a Poisson distribution whose rate depends on a document position and word-specific parameters, recovering, for example, a left–right ordering of parties purely from how often each word appears in each text.