Public debates are characterized by a complex structure that is constantly changing. As such, they pose severe challenges to traditional methodologies of analysis: techniques focusing on detecting latent patterns and structures typically require time-invariant data, while dynamic analyses are mostly limited to investigating the behavior of a few known processes over time. The Evolutionary Factor Analysis (EFA) of frames, a technique for analyzing the dynamic structure of high-dimensional semantic network data with time-varying latent structure, allows tracing subtle changes in the latent organization of a debate over time, identifying and describing the main underlying processes.
To understand what EFA is, we need to introduce the main ideas behind factor analysis for time series. The main idea behind a factor model is that a ...
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