Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling

Maxim Ifergan, Omri Abend, Renana Keydar, Amit Pinchevski

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.
Anthology ID:
2024.htres-1.7
Volume:
Proceedings of the First Workshop on Holocaust Testimonies as Language Resources (HTRes) @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Isuri Anuradha, Martin Wynne, Francesca Frontini, Alistair Plum
Venues:
htres | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
44–52
Language:
URL:
https://aclanthology.org/2024.htres-1.7
DOI:
Bibkey:
Cite (ACL):
Maxim Ifergan, Omri Abend, Renana Keydar, and Amit Pinchevski. 2024. Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling. In Proceedings of the First Workshop on Holocaust Testimonies as Language Resources (HTRes) @ LREC-COLING 2024, pages 44–52, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling (Ifergan et al., htres-WS 2024)
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