“Six Degrees of Francis Bacon” immediately caught my attention, as Francis Bacon frequently appears in Science, Technology, and Society (STS) studies, as well as in the broader fields of natural and social sciences. His name sounded familiar, and since philosophical thought is deeply interconnected with the time, space, and relationships that shape a philosopher’s life, I was intrigued by how network analysis could reveal the indirect influences behind his ideas.
In the Six Degrees of Francis Bacon project, nodes represent historical figures—such as Francis Bacon, William Shakespeare, and Isaac Newton—who lived in early modern Britain (circa 1500–1700). The edges denote inferred relationships or connections between these individuals. This primarily constitutes a single-mode network, as the nodes consist solely of historical figures, with edges representing relationships between them.
Developed through a collaboration between Carnegie Mellon University and Georgetown University, the project integrates network analysis with text mining techniques. By applying natural language processing (NLP) tools to extensive biographical texts, researchers extracted and inferred relationships between historical figures. They mined approximately 62 million words from the Oxford Dictionary of National Biography to construct a network of connections, which were then visualized in an interactive web interface. I found similarities between this project and Linked Jazz, which I explored in a previous blog post. While Linked Jazz uses oral history as a data source to map relationships between jazz musicians, Six Degrees of Francis Bacon relies on biographical texts to uncover connections in early modern Britain.
The platform allows users to:
❶Explore the network by searching for specific individuals.
❷Visualize connections between figures and observe how they are linked within the broader social network.
❸Contribute by adding or editing relationships based on scholarly research, thereby refining and expanding the dataset.
Through this interactive approach, users can uncover previously unnoticed connections and contribute their own insights. By fostering a collaborative scholarly community, the project makes historical social network analysis more dynamic, participatory, and accessible.
