@inproceedings{shokri-etal-2024-safe,
title = "Is It Safe to Tell Your Story? Towards Achieving Privacy for Sensitive Narratives",
author = "Shokri, Mohammad and
Bishop, Allison and
Levitan, Sarah Ita",
editor = "Lal, Yash Kumar and
Clark, Elizabeth and
Iyyer, Mohit and
Chaturvedi, Snigdha and
Brei, Anneliese and
Brahman, Faeze and
Chandu, Khyathi Raghavi",
booktitle = "Proceedings of the The 6th Workshop on Narrative Understanding",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wnu-1.7",
pages = "47--54",
abstract = "Evolving tools for narrative analysis present an opportunity to identify common structure in stories that are socially important to tell, such as stories of survival from domestic abuse. A greater structural understanding of such stories could lead to stronger protections against de-anonymization, as well as future tools to help survivors navigate the complex trade-offs inherent in trying to tell their stories safely. In this work we explore narrative patterns within a small set of domestic violence stories, identifying many similarities. We then propose a method to assess the safety of sharing a story based on a distance feature vector.",
}
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<abstract>Evolving tools for narrative analysis present an opportunity to identify common structure in stories that are socially important to tell, such as stories of survival from domestic abuse. A greater structural understanding of such stories could lead to stronger protections against de-anonymization, as well as future tools to help survivors navigate the complex trade-offs inherent in trying to tell their stories safely. In this work we explore narrative patterns within a small set of domestic violence stories, identifying many similarities. We then propose a method to assess the safety of sharing a story based on a distance feature vector.</abstract>
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%0 Conference Proceedings
%T Is It Safe to Tell Your Story? Towards Achieving Privacy for Sensitive Narratives
%A Shokri, Mohammad
%A Bishop, Allison
%A Levitan, Sarah Ita
%Y Lal, Yash Kumar
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Chaturvedi, Snigdha
%Y Brei, Anneliese
%Y Brahman, Faeze
%Y Chandu, Khyathi Raghavi
%S Proceedings of the The 6th Workshop on Narrative Understanding
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F shokri-etal-2024-safe
%X Evolving tools for narrative analysis present an opportunity to identify common structure in stories that are socially important to tell, such as stories of survival from domestic abuse. A greater structural understanding of such stories could lead to stronger protections against de-anonymization, as well as future tools to help survivors navigate the complex trade-offs inherent in trying to tell their stories safely. In this work we explore narrative patterns within a small set of domestic violence stories, identifying many similarities. We then propose a method to assess the safety of sharing a story based on a distance feature vector.
%U https://aclanthology.org/2024.wnu-1.7
%P 47-54
Markdown (Informal)
[Is It Safe to Tell Your Story? Towards Achieving Privacy for Sensitive Narratives](https://aclanthology.org/2024.wnu-1.7) (Shokri et al., WNU 2024)
ACL