Evolutionary correspondence analysis of the semantic dynamics of frames

Abstract

We introduce and implement a novel dimension-reduction method for high-dimensional time-varying contingency-tables: the Evolutionary Correspondence Analysis (ECA). ECA enables a comparative analysis of high-dimensional, diachronic processes by identifying a small number of shared latent variables that shape co-evolving data patterns. ECA offers new opportunities for the study of complex social phenomena, such as co-evolving public debates: Its capacity to inductively extract time-varying latent variables from observed contents of evolving debates permits an analysis of meanings shared by linked sub-discourses, such as linked national public spheres or the discourses led by distinct political camps within a shared public sphere. We illustrate the utility of our approach by studying how the Greek and German right-, centre-, and left-leaning news coverage of the European financial crisis evolved between its outbreak in 2009 until its institutional containment in 2012. Comparing the use of 525 unique concepts in six German and Greek outlets with different political leaning over an extended period of time, we identify two common factors accounting for those evolving meanings and analyse how the different sub-discourses influenced one another over time. We allow the factor loadings to be time-varying, and fit to the latent factors a time-varying vector-auto-regressive model with time-varying mean.