Detecting Intentional Self-Harm on Instagram: Development, Testing, and Validation of an Automatic Image-Recognition Algorithm to Discover Cutting-Related Posts

Abstract

Self-injurious behavior is often practiced in secrecy or involves body parts that are easy to hide, making early detection difficult and hampering intervention and treatment. However, cutting, one of the most common intentional forms of nonsuicidal self-injury (NSSI), is relatively often shared publicly via new digital media technologies. We explored NSSI on Instagram through a pioneering combination of two computational methods: First, we developed an automatic image-recognition algorithm that uncovered NSSI (or the absence of NSSI) in digital pictures, and second, we employed web-scraping techniques to obtain all pictures posted on Instagram in a given time frame under four NSSI-related hashtags in English and German. The image-recognition algorithm was then used to explore the relative prevalence of NSSI in these N = 13,132 pictures posted within 48 hr on Instagram under #cutting ( n = 4,219), #suicide ( n = 7,910), #selbstmord ( n = 173), and #ritzen ( n = 830) in June 2018. This article not only aims to raise awareness of NSSI on Instagram but also introduces the first automatic image-recognition algorithm that addresses cutting on social media and presents that algorithm’s first empirical test run on a large sample of pictures scraped from Instagram. The ultimate goal of this research is to protect vulnerable populations from contact with NSSI-related pictures posted on social media.