TY  - JOUR
AU  - Das, Sudeshna
AU  - Walker, Drew
AU  - Rajwal, Swati
AU  - Lakamana, Sahithi
AU  - Sumner, Steven A
AU  - Mack, Karin A
AU  - Kaczkowski, Wojciech
AU  - Sarker, Abeed
PY  - 2024
DA  - 2024/5/2
TI  - Emerging Trends of Self-Harm Using Sodium Nitrite in an Online Suicide Community: Observational Study Using Natural Language Processing Analysis
JO  - JMIR Ment Health
SP  - e53730
VL  - 11
KW  - online suicide community
KW  - suicide
KW  - sodium nitrite
KW  - sodium nitrite sources
KW  - mental health
KW  - adolescent
KW  - juvenile
KW  - self harm
KW  - Sanctioned Suicide
KW  - online forum
KW  - US
KW  - public health
KW  - surveillance
KW  - data mining
KW  - natural language processing
KW  - machine learning
KW  - usage
KW  - suicidal
KW  - accuracy
KW  - consumption
KW  - information
KW  - United States
AB  - Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums. 
SN  - 2368-7959
UR  - https://mental.jmir.org/2024/1/e53730
UR  - https://doi.org/10.2196/53730
DO  - 10.2196/53730
ID  - info:doi/10.2196/53730
ER  -