The Shape of Testimony: A Scalable Framework for Oral History Archive Comparison

TL;DR

This study introduces a scalable computational framework to analyze and compare oral history archives, revealing nuanced differences and overlaps in testimony styles across two major Holocaust survivor collections.

Contribution

It provides a novel, scalable methodology combining discourse segmentation, topic modeling, and LLM analysis for comparative oral history research.

Findings

Confirmed structural differences between collections

Identified significant overlaps in narrative patterns

Challenged the simple structured vs. free-form dichotomy

Abstract

Researchers in Holocaust studies have often distinguished between two styles of oral survivor testimony: the USC Shoah Foundation's interviews tend to follow a structured, interviewer-guided format, whereas the Yale Fortunoff Video Archive generally favors a more free-form, open-ended style. This distinction has influenced both scholarly research and the development of later archives. In this study, we critically examine that claim by conducting a large-scale computational analysis of more than 1,600 testimonies from both collections. Leveraging discourse segmentation, topic modeling, and large language model (LLM) based analysis, we quantify the "structuredness" level of testimonies through topic coherence, interviewer-survivor dynamics, and the distribution of question types. Our results generally corroborate the structural differences identified in earlier research, while also revealing significant overlaps between the collections, both within individual interviews and across common narrative patterns. This complicates the simple "structured vs. free-form" dichotomy often applied to these oral histories. Beyond revisiting a foundational claim in Holocaust studies, our work provides a scalable, replicable framework for comparative corpus analysis. As a proof of concept, it suggests broader applications for digital oral history, narrative analysis, and the design of citizen-science annotation platforms.