Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling
It addresses the problem of manual analysis limitations for historians and researchers studying Holocaust testimonies, though it is incremental as it applies existing NLP methods to a specific historical dataset.
This paper tackled the challenge of analyzing Holocaust survivor testimonies by applying BERTopic-based topic modeling to identify key themes and narrative patterns, revealing common schemas and divergences based on age and gender, and introducing a method to detect atypical testimonies with results showing unique insights into survivor experiences.
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.