Trigger Warnings: A Computational Approach to Understanding User-Tagged Trigger Warnings

Sarthak Tyagi, Adwita Arora, Krish Chopra, Manan Suri

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
Anthology ID:
2023.ranlp-stud.5
Volume:
Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing
Month:
September
Year:
2023
Address:
Varna, Bulgaria
Editors:
Momchil Hardalov, Zara Kancheva, Boris Velichkov, Ivelina Nikolova-Koleva, Milena Slavcheva
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd., Shoumen, Bulgaria
Note:
Pages:
44–54
Language:
URL:
https://aclanthology.org/2023.ranlp-stud.5
DOI:
Bibkey:
Cite (ACL):
Sarthak Tyagi, Adwita Arora, Krish Chopra, and Manan Suri. 2023. Trigger Warnings: A Computational Approach to Understanding User-Tagged Trigger Warnings. In Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing, pages 44–54, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
Cite (Informal):
Trigger Warnings: A Computational Approach to Understanding User-Tagged Trigger Warnings (Tyagi et al., RANLP 2023)
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PDF:
https://preview.aclanthology.org/naacl24-info/2023.ranlp-stud.5.pdf
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