The art of connections: constructing a social network from the correspondence archive of Sybren Valkema

TL;DR

This paper presents an automated method for constructing and analyzing a social network from historical correspondence archives, demonstrating its effectiveness and challenges in extracting meaningful connections from complex, multi-language documents.

Contribution

The authors develop an end-to-end pipeline for extracting social networks from historical archives, combining text recognition, entity extraction, and classification, validated against expert analysis.

Findings

Strong performance in identifying sender-receiver connections

Ability to discover additional meaningful connections

Main challenge remains in text recognition accuracy

Abstract

Social network analysis allows researchers to discover insights from connections between people. While the process of building a social network is relatively straightforward for contemporary social media, deriving connections from historical archives remains a challenging task, with every data collection presenting its unique challenges. Our contribution focuses on building and analysing a social network from the correspondence archive of Sybren Valkema (1916-1996), a Dutch glass artist and educator. The archive contains both typewritten and handwritten documents in multiple languages, and includes letters from glass artists, art students, art collectors and other agents. We develop an automatic pipeline approach which includes separating handwritten and typed documents, performing text recognition specific to the document modality, extracting names of people from text using named entity recognition, de-duplicating the resulting names to create actor nodes, classifying the actors using entity linking, and, finally, connecting them together and analysing the resulting network. Every part of the pipeline is evaluated against a manual analysis performed by an art historian on a subset of the data collection in order to find out which pitfalls of the automatic approach need to be resolved in future work and, on the contrary, whether using the automatic approach allows to discover any additional insights. The results show strong performance in discovering sender-receiver connections as well as additional meaningful connections in text, with the main challenge being text recognition on scanned pages.