#selfharm on Instagram: Quantitative Analysis and Classification of Non-Suicidal Self-Injury

Abstract

Non-Suicidal Self-Injury (NSSI) is the intentional destruction of body tissue without the intent to die. NSSI is particularly prevalent among adolescents and young adults as a means of emotional regulation. With the proliferation of social media, NSSI content is frequently being posted, viewed, and shared on popular social media platforms, which may increase social contagion among adolescents. To address this problem, this work first quantifies the prevalence of NSSI content on social media. We develop a content crawler that searches for posts, images, and videos with NSSI-related hashtags (e.g., #selfharm), downloads NSSI content from target social media platforms, and stores them in cloud storage. We then perform a trend analysis, which confirms a steep increase in NSSI posts on social media. Moreover, this work develops an image classifier to identify NSSI or non-NSSI images from social media content. Our classifier is based on the idea of weakly supervised object localization. We evaluate our NSSI classifier with more than 30K labeled NSSI images collected from social media. In our evaluation, our classifier accurately identifies NSSI images with 94% accuracy, and it outperforms state-of-the-art pre-trained models. An accurate NSSI image classifier is an essential first step to enable us and/or social media providers to protect adolescents and young adults from social contagion due to NSSI exposure through such actions as legitimate filtering mechanisms.