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In this paper we present a statistical method for inferring historical social networks from biographical documents as well as the scholarly aims for doing so. Existing scholarship on historical social networks is scattered across an unmanageable number of disparate books and articles. A researcher interested in how persons were connected to one another in our field of study, early modern Britain (c. 1500-1700), has no global, unified resource to which to turn. Manually building such a network is infeasible, since it would need to represent thousands of nodes and tens of millions of potential edges just to include the relations among the most prominent persons of the period. Our Six Degrees of Francis Bacon project takes up recent statistical techniques and digital tools to reconstruct and visualize the early modern social network. We describe in this paper the natural language processing tools and statistical graph learning techniques that we used to extract names and infer relations from the Oxford Dictionary of National Biography. We then explain the steps taken to test inferred relations against the knowledge of experts in order to improve the accuracy of the learning techniques. Our argument here is twofold: first, that the results of this process, a global visualization of Britain’s early modern social network, will be useful to scholars and students of the period; second, that the pipeline we have developed can, with local modifications, be reused by other scholars to generate networks for other historical or contemporary societies from biographical documents.
Warren et al. (Tue,) studied this question.