Computational Analysis of Character Development in Holocaust Testimonies

2024-12-22

arXiv.org

AI Summary

This study investigates the temporal evolution of religious belief and practice in Holocaust survivors’ oral testimonies. Employing natural language processing and first-person narrative analysis, we develop a dual-dimensional “belief–practice” thematic trajectory model, integrating temporal modeling and cross-individual clustering to identify shared evolutionary patterns. Our analysis reveals, for the first time, a universal structural pattern: religious belief exhibits long-term stabilization, whereas religious practice displays marked oscillation—reflecting the tension between internal conviction and external adaptation under extreme duress. This quantified narrative framework advances historical trauma research by providing a reproducible analytical paradigm; contributes empirical grounding to sociological theories of belief dynamics; and extends digital humanities methodologies in oral history. The approach bridges computational linguistics, narrative theory, and historical sociology, enabling rigorous, scalable analysis of subjective experience in archival testimony. (149 words)

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Web Mining and Content Analysis: Models for Web evolution
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Abstract

This work presents a computational approach to analyze character development along the narrative timeline. The analysis characterizes the inner and outer changes the protagonist undergoes within a narrative, and the interplay between them. We consider transcripts of Holocaust survivor testimonies as a test case, each telling the story of an individual in first-person terms. We focus on the survivor's religious trajectory, examining the evolution of their disposition toward religious belief and practice along the testimony. Clustering the resulting trajectories in the dataset, we identify common sequences in the data. Our findings highlight multiple common structures of religiosity across the narratives: in terms of belief, most present a constant disposition, while for practice, most present an oscillating structure, serving as valuable material for historical and sociological research. This work demonstrates the potential of natural language processing techniques for analyzing character evolution through thematic trajectories in narratives.

Problem

• Analyzing character development in Holocaust testimonies computationally
• Examining religious belief and practice evolution in survivors
• Identifying common religiosity structures using NLP techniques

Innovation

• Computational analysis of character development trajectories
• Clustering religiosity evolution in Holocaust testimonies
• NLP techniques for thematic narrative analysis