Emerging Trends of Self-Harm Using Sodium Nitrite in an Online Suicide Community: Observational Study Using Natural Language Processing Analysis

• Published in: JMIR mental health Vol. 11; p. e53730
• Main Authors: Das, Sudeshna, Walker, Drew, Rajwal, Swati, Lakamana, Sahithi, Sumner, Steven A, Mack, Karin A, Kaczkowski, Wojciech, Sarker, Abeed
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 02.05.2024
• Subjects: Adult; Child & adolescent mental health; Female; Firearms; Humans; Internet; Male; Natural Language Processing; Observational studies; Self destructive behavior; Self-Injurious Behavior - epidemiology; Social Media; Social networks; Sodium Nitrite; Suicide - psychology; Suicide - trends; Suicides & suicide attempts; Teenagers; Trends; Young Adult; United States > US; natural language processing; United States; online suicide community; data mining; mental health; suicide; usage; online forum; accuracy; consumption; machine learning; Sanctioned Suicide; suicidal; sodium nitrite; adolescent; sodium nitrite sources; information; juvenile; surveillance; US; public health; self harm
• ISSN: 2368-7959; 2368-7959
• DOI: 10.2196/53730

There is growing concern around the use of sodium nitrite (SN) as an emerging means of suicide, particularly among younger people. Given the limited information on the topic from traditional public health surveillance sources, we studied posts made to an online suicide discussion forum, "Sanctioned Suicide," which is a primary source of information on the use and procurement of SN. This study aims to determine the trends in SN purchase and use, as obtained via data mining from subscriber posts on the forum. We also aim to determine the substances and topics commonly co-occurring with SN, as well as the geographical distribution of users and sources of SN. We collected all publicly available from the site's inception in March 2018 to October 2022. Using data-driven methods, including natural language processing and machine learning, we analyzed the trends in SN mentions over time, including the locations of SN consumers and the sources from which SN is procured. We developed a transformer-based source and location classifier to determine the geographical distribution of the sources of SN. Posts pertaining to SN show a rise in popularity, and there were statistically significant correlations between real-life use of SN and suicidal intent when compared to data from the Centers for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiologic Research (⍴=0.727; P<.001) and the National Poison Data System (⍴=0.866; P=.001). We observed frequent co-mentions of antiemetics, benzodiazepines, and acid regulators with SN. Our proposed machine learning-based source and location classifier can detect potential sources of SN with an accuracy of 72.92% and showed consumption in the United States and elsewhere. Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums.