PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums.
摘要：
■人们越来越关注使用亚硝酸钠（SN）作为一种新兴的自杀手段，尤其是年轻人。鉴于传统公共卫生监测来源关于该主题的信息有限，我们研究了一个网上自杀论坛的帖子，\"被制裁的自杀,“这是有关SN使用和采购的主要信息来源。
■本研究旨在确定SN购买和使用的趋势，通过数据挖掘从论坛上的订阅者帖子获得。我们还旨在确定与SN共同出现的物质和主题，以及SN的用户和来源的地理分布。
■我们收集了该网站于2018年3月成立至2022年10月的所有公开可用信息。使用数据驱动方法，包括自然语言处理和机器学习，我们分析了SN提及随着时间的推移，包括SN消费者的位置和采购SN的来源。我们开发了基于变压器的源和位置分类器，以确定SN源的地理分布。
■与SN有关的帖子显示受欢迎程度上升，与疾病控制和预防中心（CDC）广泛的流行病学研究在线数据（=0.727;P<.001）和国家毒物数据系统（=0.866;P=.001）的数据相比，SN的实际使用与自杀意图之间存在统计学上的显着相关性。我们观察到止吐药的频繁出现，苯二氮卓类药物,和具有SN的酸调节剂。我们提出的基于机器学习的源和位置分类器可以检测到潜在的SN源，准确率为72.92％，并显示在美国和其他地方的消费。
■可以从在线论坛获得有关SN和其他新兴自杀机制的重要信息。
■本研究旨在确定SN购买和使用的趋势，通过数据挖掘从论坛上的订阅者帖子获得。我们还旨在确定与SN共同出现的物质和主题，以及SN的用户和来源的地理分布。
■我们收集了该网站于2018年3月成立至2022年10月的所有公开可用信息。使用数据驱动方法，包括自然语言处理和机器学习，我们分析了SN提及随着时间的推移，包括SN消费者的位置和采购SN的来源。我们开发了基于变压器的源和位置分类器，以确定SN源的地理分布。
■与SN有关的帖子显示受欢迎程度上升，与疾病控制和预防中心（CDC）广泛的流行病学研究在线数据（
■可以从在线论坛获得有关SN和其他新兴自杀机制的重要信息。
