Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling

TL;DR

This study uses advanced NLP techniques, specifically topic modeling with BERTopic, to analyze Holocaust testimonies, revealing common narrative structures, subgroup divergences, and outliers with atypical topic patterns.

Contribution

It introduces a novel method for detecting testimonies with unusual topic distributions, enhancing understanding of survivor narratives through NLP.

Findings

Identified key themes across testimonies

Revealed narrative divergences by age and gender

Detected outlier testimonies with atypical topic patterns

Abstract

The vast collection of Holocaust survivor testimonies presents invaluable historical insights but poses challenges for manual analysis. This paper leverages advanced Natural Language Processing (NLP) techniques to explore the USC Shoah Foundation Holocaust testimony corpus. By treating testimonies as structured question-and-answer sections, we apply topic modeling to identify key themes. We experiment with BERTopic, which leverages recent advances in language modeling technology. We align testimony sections into fixed parts, revealing the evolution of topics across the corpus of testimonies. This highlights both a common narrative schema and divergences between subgroups based on age and gender. We introduce a novel method to identify testimonies within groups that exhibit atypical topic distributions resembling those of other groups. This study offers unique insights into the complex narratives of Holocaust survivors, demonstrating the power of NLP to illuminate historical discourse and identify potential deviations in survivor experiences.