  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.
