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Global network of central banks and central bankers

How central banks and central bankers are connected? I extract the most mentioned entities in the central banks’ communication from 2007 to 2021 to understand patterns of their interaction and coinfluence.
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Data: Central banks’ speeches from the BIS site. Scraped with Selenium (Python) in the end of 2021.

Time frame: 2007 – 2021.

Analysis: preprocessing (spaCy), named-entity recognition (spaCy), network creation (NetworkX), Louvain method for community detection.

Visualization: Pyvis (organisations) and (persons) [suggested to use Force Atlas2 solver].

Description: I build on a corpus of central bankers’ speeches from 2007 to 2021.

In the analysis, I use spaCy to parse named entities (i.e., real-world objects, such as organizations, people, and geographic locations). In particular, I focus on organizations and persons to understand which entities/personalities central banks refer to the most in their communication. As a result of parsing, I get a table that connects a central bank that delivers communication (source column) with organizations/personalities that are mentioned in this particular communication (target column). I exclude all self-references, automatically (using ECB abbreviations glossary) and manually resolve the most frequent coreferences (i.e., different spellings of the same entity, such as “us federal reserve”, “federal reserve system”, “fed”), and apply a threshold of 10 word occurrences.

Using the source-target table, I generate two networks in NetworkX and visualize them as interactive graphs with Pyvis, one for organizations and another for personalities.