Publications – CCMI: Center for Computational Media Intelligence

2022

Tóth, Gábor Mihály; Hempel, Tim; Somandepalli, Krishna; Narayanan, Shrikanth

Studying Large-Scale Behavioral Differences in Auschwitz-Birkenau with Simulation of Gendered Narratives Journal Article

In: Digital Humanities Quarterly, 16 (3), 2022.

Abstract | Links | BibTeX | Tags: Auschwitz, computational narrative modeling, survivor testimonies

@article{Tóth2022,

title = {Studying Large-Scale Behavioral Differences in Auschwitz-Birkenau with Simulation of Gendered Narratives},

author = {Gábor Mihály Tóth and Tim Hempel and Krishna Somandepalli and Shrikanth Narayanan},

url = {http://www.digitalhumanities.org/dhq/vol/16/3/000622/000622.html},

year  = {2022},

date = {2022-08-29},

urldate = {2022-08-29},

journal = {Digital Humanities Quarterly},

volume = {16},

number = {3},

abstract = {In Auschwitz-Birkenau men and women were detained separately; anecdotal evidence suggests that they behaved differently. However, producing evidence based insights into victims' behavior is challenging. Perpetrators frequently destroyed camp documentations; victims' perspective remains dispersed in thousands of oral history interviews with survivors. Listening to, watching, or reading these thousands of interviews is not viable, and there is no established computational approach to gather systematic evidence from a large number of interviews. In this study, by applying methods and concepts of molecular physics, we developed a conceptual framework and computational approach to study thousands of human stories and we investigated 6628 interviews by survivors of the Auschwitz-Birkenau death camp. We applied the concept of state space and the Markov State Model to model the ensemble of 6628 testimonies. The Markov State Model along with the Transition Path Theory allowed us to compare the way women and men remember their time in the camp. We found that acts of solidarity and social bonds are the most important topics in their testimonies. However, we found that women are much more likely to address these topics. We provide systematic evidence that not only were women more likely to recall solidarity and social relations in their belated testimonies but they were also more likely to perform acts of solidarity and form social bonds in Auschwitz-Birkenau. Oral history interviews with Holocaust survivors constitute an important digital cultural heritage that documents one of the darkest moments in human history; generally, oral history collections are ubiquitous sources of modern history and significant assets of libraries and archives. We anticipate that our conceptual and computational framework will contribute not only to the understanding of gender behavior but also to the exploration of oral history as a cultural heritage, as well as to the computational study of narratives. This paper presents novel synergies between history, computer science, and physics, and it aims to stimulate further collaborations between these fields.},

keywords = {Auschwitz, computational narrative modeling, survivor testimonies},

pubstate = {published},

tppubtype = {article}

}

In Auschwitz-Birkenau men and women were detained separately; anecdotal evidence suggests that they behaved differently. However, producing evidence based insights into victims' behavior is challenging. Perpetrators frequently destroyed camp documentations; victims' perspective remains dispersed in thousands of oral history interviews with survivors. Listening to, watching, or reading these thousands of interviews is not viable, and there is no established computational approach to gather systematic evidence from a large number of interviews. In this study, by applying methods and concepts of molecular physics, we developed a conceptual framework and computational approach to study thousands of human stories and we investigated 6628 interviews by survivors of the Auschwitz-Birkenau death camp. We applied the concept of state space and the Markov State Model to model the ensemble of 6628 testimonies. The Markov State Model along with the Transition Path Theory allowed us to compare the way women and men remember their time in the camp. We found that acts of solidarity and social bonds are the most important topics in their testimonies. However, we found that women are much more likely to address these topics. We provide systematic evidence that not only were women more likely to recall solidarity and social relations in their belated testimonies but they were also more likely to perform acts of solidarity and form social bonds in Auschwitz-Birkenau. Oral history interviews with Holocaust survivors constitute an important digital cultural heritage that documents one of the darkest moments in human history; generally, oral history collections are ubiquitous sources of modern history and significant assets of libraries and archives. We anticipate that our conceptual and computational framework will contribute not only to the understanding of gender behavior but also to the exploration of oral history as a cultural heritage, as well as to the computational study of narratives. This paper presents novel synergies between history, computer science, and physics, and it aims to stimulate further collaborations between these fields.