@inproceedings{tyagi-etal-2023-trigger,
title = "Trigger Warnings: A Computational Approach to Understanding User-Tagged Trigger Warnings",
author = "Tyagi, Sarthak and
Arora, Adwita and
Chopra, Krish and
Suri, Manan",
editor = "Hardalov, Momchil and
Kancheva, Zara and
Velichkov, Boris and
Nikolova-Koleva, Ivelina and
Slavcheva, Milena",
booktitle = "Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ranlp-stud.5",
pages = "44--54",
abstract = "Content and trigger warnings give information about the content of material prior to receiving it and are used by social media users to tag their content when discussing sensitive topics. Trigger warnings are known to yield benefits in terms of an increased individual agency to make an informed decision about engaging with content. At the same time, some studies contest the benefits of trigger warnings suggesting that they can induce anxiety and reinforce the traumatic experience of specific identities. Our study involves the analysis of the nature and implications of the usage of trigger warnings by social media users using empirical methods and machine learning. Further, we aim to study the community interactions associated with trigger warnings in online communities, precisely the diversity and content of responses and inter-user interactions. The domains of trigger warnings covered will include self-harm, drug abuse, suicide, and depression. The analysis of the above domains will assist in a better understanding of online behaviour associated with them and help in developing domain-specific datasets for further research",
}
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%0 Conference Proceedings
%T Trigger Warnings: A Computational Approach to Understanding User-Tagged Trigger Warnings
%A Tyagi, Sarthak
%A Arora, Adwita
%A Chopra, Krish
%A Suri, Manan
%Y Hardalov, Momchil
%Y Kancheva, Zara
%Y Velichkov, Boris
%Y Nikolova-Koleva, Ivelina
%Y Slavcheva, Milena
%S Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F tyagi-etal-2023-trigger
%X Content and trigger warnings give information about the content of material prior to receiving it and are used by social media users to tag their content when discussing sensitive topics. Trigger warnings are known to yield benefits in terms of an increased individual agency to make an informed decision about engaging with content. At the same time, some studies contest the benefits of trigger warnings suggesting that they can induce anxiety and reinforce the traumatic experience of specific identities. Our study involves the analysis of the nature and implications of the usage of trigger warnings by social media users using empirical methods and machine learning. Further, we aim to study the community interactions associated with trigger warnings in online communities, precisely the diversity and content of responses and inter-user interactions. The domains of trigger warnings covered will include self-harm, drug abuse, suicide, and depression. The analysis of the above domains will assist in a better understanding of online behaviour associated with them and help in developing domain-specific datasets for further research
%U https://aclanthology.org/2023.ranlp-stud.5
%P 44-54
Markdown (Informal)
[Trigger Warnings: A Computational Approach to Understanding User-Tagged Trigger Warnings](https://aclanthology.org/2023.ranlp-stud.5) (Tyagi et al., RANLP 2023)
ACL