Trigger Warnings: A Computational Approach to Understanding User-Tagged Trigger Warnings (2023)

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

## introduction trigger warnings are "a statement at the start of a piece of writing, video, etc. alerting the reader or viewer to the fact that it contains potentially distressing material-often used to introduce a description of such content" @xcite . they are used frequently by users online when discussing sensitive issues which might trigger a detrimental response in certain users. trigger warnings are used in multiple contexts with respect to sensitive content; common examples include narrating one's own experience, talking about someone else's experience, or discussing content that might be sensitive @xcite ; @xcite . some domains that are commonly associated with the use of trigger warnings include self-harm, violence, drug abuse, suicide, and depression. trigger warnings thus can be used as a tool for others to self-moderate the content that they are engaging with on the internet with their level of comfort. however, recent empirical studies have shown that trigger warnings may also lead to the centering of traumatic experiences in communities, thus having the exact opposite effect @xcite . while in the wrong circumstances, anything can act as a trigger for a person under distress, some content has a universally accepted nature of being distressing or troubling for a large group of people 1 @xcite ; @xcite . an overwhelming consensus on the classification and typology of these content warnings does not exist, as some sources like providing a more general description of content and trigger warnings (as an example considering violence as the trigger warning). in contrast, others look at the sub-categories as a more appropriate way of describing the nature of the content (animal cruelty, and sexual violence all describe a sub-category of violence, but give the reader a better idea of the nature the content represents). nevertheless, these sources agree upon the fact that none of their lists represents an exhaustive account of content that can distress a reader. through our research, we want to utilize publicly available data from social media to perform an analysis on the use of trigger warnings, the nature of discourse associated with the selected domains, and community modelling of online communities. the analysis would involve the linguistic study of the expressions demonstrated by users. it would also include topic modelling and keyword analysis to study the nature of posts. a comparison between different domains would help us understand the differences between the communities involved in the respective domain. an additional area of analysis includes the response received to posts tagged with trigger warnings. in our study, we build up a novel dataset of trigger warning posts from various subreddits on reddit, with our target trigger warnings being self-harm, suicidal ideation, depression, and drug abuse. we perform a multitude of analyses on this extracted data, which includes sentiment analysis of the gathered posts, an analysis of how the frequency of posts in these different subreddits have evolved, extraction of keyphrases from these texts, a look into how common topic models perform in this analysis and finally we tackle a classification problem by assigning classes to the dataset based on what type of post (post asking questions, a post asking advice, rant posts, etc.). we plan to release our dataset after paper acceptance. ## related work while we are not aware of prior work on computational trigger warning analysis using social media data or specifically warning assignments, we have divided our related work into sections highlighting the advantages of bertopic over traditional topic modelling techniques, relevant tasks to our objective, and other relevant papers. ## methodology in this section we will briefly explain our data collection and pre-processing techniques, exploratory data analysis to emphasize the increasing trend of trigger warning posts online, keyword and keyphrase analysis of the collected data, and evaluate various topic generation metrics to generalize the topic modelling quality and benchmark classification performance to predict the intent of posts mentioning trigger warnings. the entire framework is described in fig 1 . ## conclusions in this paper, we present an initial study of evaluating and inspecting data containing trigger warnings in social network groups dealing with different clinical disorders. we present a unique dataset that contains user-level mentions of trigger warnings present in their content, exploratory data analysis measuring the sentiment across posts using statistical and lexical techniques, trends of such posts