TY -非盟的姚明,汉娜盟——Rashidian新浪盟——咚,鑫盟——Duanmu弘益盟-罗森塔尔,理查德·N AU - Wang) PY - 2020 DA - 2020/11/27 TI -检测自杀Reddit上阿片用户包括:基于机器学习方法乔- J地中海互联网Res SP - e15293六世- 22 - 11 KW -阿片类药物流行病千瓦opioid-related障碍KW -自杀KW -社会媒体KW -机器学习KW -深度学习KW -自然语言处理AB -背景:近年来，自杀率和吸毒过量率都在上升。许多与阿片类药物使用障碍作斗争的人容易产生自杀意念;这通常会导致用药过量。然而，这些致命的过量服用很难归类为有意或无意。故意过量服用很难被发现，部分原因是缺乏预测因素和社会污名，导致个人不愿寻求帮助。这些人可能会转而使用网络手段来表达他们的担忧。目的:本研究旨在利用机器学习方法提取阿片类药物使用者在Reddit上的自杀帖子。模型的性能是数据纯度的衍生品，结果将帮助我们更好地理解这些用户的基本原理，为阿片类药物流行的一部分提供新的见解。方法:从2017年6月至2018年6月间的Reddit帖子中收集r/suicidewatch, r/depression，一组与阿片类药物相关的Reddit子版块和一组对照子版块。 We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data. Results: Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments. Conclusions: Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target. SN - 1438-8871 UR - //www.mybigtv.com/2020/11/e15293/ UR - https://doi.org/10.2196/15293 UR - http://www.ncbi.nlm.nih.gov/pubmed/33245287 DO - 10.2196/15293 ID - info:doi/10.2196/15293 ER -