Trigger Warning

Trigger warnings, labels alerting readers to potentially distressing content, are the subject of growing research interest. Current studies focus on automatically identifying passages or documents requiring warnings, employing machine learning models like SVMs and BERT, as well as more complex architectures such as fine-tuned RoBERTa, to analyze text and classify the presence of various triggers, including violence and sexual abuse. This research aims to improve the accuracy and efficiency of trigger warning assignment, potentially leading to better content moderation and support for vulnerable individuals. The development of robust automated systems could have significant implications for online platforms and mental health resources.