Description
In this talk, I will showcase a project that combines several methods to analyze the complex dynamic structure of front pages in historical newspapers with varying ideological backgrounds. This project applies natural language processing, information theory, and time series analysis to examine a coarse-grained representation of the news. The project draws from a wide range of Python packages, which are combined in an adaptable workflow. Rather than highlighting one particular method, I will reflect on the process of constructing a larger workflow and how this can be used for historical research.