In organizational theory, institutionalists generally make predictions of corresponding context and policy outcome based on structural processes. Psychoanalytic theory, in contrast, focuses on the rhetorical framing rather than the environment of a policy for predictive outcomes. This study aims to explore the debate over policy prediction by developing a supervised machine learning model to predict for policy success and context in the United Nations (UN). Through data collected with a python web scraper on all UN meetings in the General Assembly (GA) and Security Council (SC) between 1994 and 2020, we parse motions, policies, and conflict indicators, before passing meeting records through the Linguistic Inquiry and Word Count (LIWC) psycholinguistic algorithm. Next, we build 12 different machine learning models to predict for policy passage and context using preprocessed motion and LIWC data; results demonstrate that the psychoanalytic models better predicted for both context and policy outcomes than the institutionalist models, suggesting that the classical political axiom, “actions speak louder than words,” may not be supported by the empirical evidence.

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