Patterns in language: Text analysis of government reports on the Irish industrial school system with word embedding

Abstract

Industrial Memories is a digital humanities initiative to supplement close readings of a government report with new distant readings, using text analytics techniques. The Ryan Report (2009), the official report of the Commission to Inquire into Child Abuse (CICA), details the systematic abuse of thousands of children from 1936 to 1999 in residential institutions run by religious orders and funded and overseen by the Irish State. Arguably, the sheer size of the Ryan Report—over 1 million words—warrants a new approach that blends close readings to witness its findings, with distant readings that help surface system-wide findings embedded in the Report. Although CICA has been lauded internationally for its work, many have critiqued the narrative form of the Ryan Report, for obfuscating key findings and providing poor systemic, statistical summaries that are crucial to evaluating the political and cultural context in which the abuse took place (Keenan, 2013, Child Sexual Abuse and the Catholic Church: Gender, Power, and Organizational Culture. Oxford University Press). In this article, we concentrate on describing the distant reading methodology we adopted, using machine learning and text-analytic methods and report on what they surfaced from the Report. The contribution of this work is threefold: (i) it shows how text analytics can be used to surface new patterns, summaries and results that were not apparent via close reading, (ii) it demonstrates how machine learning can be used to annotate text by using word embedding to compile domain-specific semantic lexicons for feature extraction and (iii) it demonstrates how digital humanities methods can be applied to an official state inquiry with social justice impact.

Developing a sentence level fairness metric using word embeddings

“…Recent work by Kozlowski et al ( 2019 ), for example, employed word embeddings to separate associations carrying cultural meaning across different time periods, e.g., rich – poor , affluence – poverty. Word embeddings have also been applied to official state inquiries on social justice (Leavy et al, 2019 ), as well as on texts related to the COVID-19 epidemic (Aiello et al, 2021 ). Likewise, Jha et al ( 2020 ) use them to measure popular sentiment towards finance across several decades in which they consider dimensions such as the “ financial system hurts the economy- financial system helps the economy” to separate between attitudes.…”