A Machine Learning Approach to Quantitative Prosopography

TL;DR

This paper introduces a machine learning framework that automatically constructs a people gazetteer from noisy newspaper texts, enabling quantitative prosopographical analysis of influential individuals in historical research.

Contribution

It presents a novel machine learning approach for automatically creating a people gazetteer from noisy textual data, facilitating quantitative prosopography.

Findings

Successfully identified influential historical figures from newspaper data

Developed a custom Influential Person Index (IPI) for ranking individuals

Analyzed 14,020 articles from 1896 New York newspaper

Abstract

Prosopography is an investigation of the common characteristics of a group of people in history, by a collective study of their lives. It involves a study of biographies to solve historical problems. If such biographies are unavailable, surviving documents and secondary biographical data are used. Quantitative prosopography involves analysis of information from a wide variety of sources about "ordinary people". In this paper, we present a machine learning framework for automatically designing a people gazetteer which forms the basis of quantitative prosopographical research. The gazetteer is learnt from the noisy text of newspapers using a Named Entity Recognizer (NER). It is capable of identifying influential people from it by making use of a custom designed Influential Person Index (IPI). Our corpus comprises of 14020 articles from a local newspaper, "The Sun", published from New York in 1896. Some influential people identified by our algorithm include Captain Donald Hankey (an English soldier), Dame Nellie Melba (an Australian operatic soprano), Hugh Allan (a Canadian shipping magnate) and Sir Hugh John McDonald (the first Prime Minister of Canada).