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Social Media Based Analysis of Opioid Epidemic Using Reddit.基于Reddit的社交媒体对阿片类药物流行情况的分析
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2
Utilizing social media to explore overdose and HIV/HCV risk behaviors among current opioid misusers.利用社交媒体探索当前阿片类药物滥用者的过量用药和 HIV/HCV 风险行为。
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Benzodiazepine Boom: Tracking Etizolam, Pyrazolam, and Flubromazepam from Pre-UK Psychoactive Act 2016 to Present Using Analytical and Social Listening Techniques.苯二氮䓬热潮:运用分析和社交倾听技巧追踪2016年英国《精神活性物质法案》出台前至今的依替唑仑、吡唑仑和氟溴西泮。
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本文引用的文献

1
Detecting Opioid-Related Aberrant Behavior using Natural Language Processing.使用自然语言处理技术检测与阿片类药物相关的异常行为。
AMIA Annu Symp Proc. 2018 Apr 16;2017:1179-1185. eCollection 2017.
2
Large-scale Analysis of Opioid Poisoning Related Hospital Visits in New York State.纽约州阿片类药物中毒相关医院就诊情况的大规模分析
AMIA Annu Symp Proc. 2018 Apr 16;2017:545-554. eCollection 2017.
3
Deep Learning Solutions for Classifying Patients on Opioid Use.用于对使用阿片类药物的患者进行分类的深度学习解决方案。
AMIA Annu Symp Proc. 2018 Apr 16;2017:525-534. eCollection 2017.
4
Patient narratives in Yelp reviews offer insight into opioid experiences and the challenges of pain management.Yelp评论中的患者叙述提供了对阿片类药物使用体验以及疼痛管理挑战的洞察。
Pain Manag. 2018 Mar 1;8(2):95-104. doi: 10.2217/pmt-2017-0050. Epub 2018 Feb 16.
5
The medical profession and stigma against people who use drugs.医学专业与对吸毒者的污名化。
Br J Psychiatry. 2017 Dec;211(6):400. doi: 10.1192/bjp.211.6.400.
6
Social networking online to recover from opioid use disorder: A study of community interactions.社交网络在线戒除阿片类药物使用障碍:社区互动研究。
Drug Alcohol Depend. 2017 Dec 1;181:5-10. doi: 10.1016/j.drugalcdep.2017.09.010. Epub 2017 Oct 5.
7
Complexities in understanding and addressing the serious public health issues related to the nonmedical use of prescription drugs.在理解和解决与处方药非医疗使用相关的严重公共卫生问题方面存在复杂性。
Addict Behav. 2017 Feb;65:215-217. doi: 10.1016/j.addbeh.2016.09.002. Epub 2016 Sep 9.
8
Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning.使用无监督机器学习探索推特圈中处方药非医疗用途和多药滥用的趋势。
Addict Behav. 2017 Feb;65:289-295. doi: 10.1016/j.addbeh.2016.08.019. Epub 2016 Aug 17.
9
A heuristic approach to determine an appropriate number of topics in topic modeling.一种用于确定主题建模中合适主题数量的启发式方法。
BMC Bioinformatics. 2015;16 Suppl 13(Suppl 13):S8. doi: 10.1186/1471-2105-16-S13-S8. Epub 2015 Sep 25.
10
The Canary in the Coal Mine Tweets: Social Media Reveals Public Perceptions of Non-Medical Use of Opioids.煤矿里的金丝雀推特:社交媒体揭示公众对阿片类药物非医疗用途的看法。
PLoS One. 2015 Aug 7;10(8):e0135072. doi: 10.1371/journal.pone.0135072. eCollection 2015.

基于Reddit的社交媒体对阿片类药物流行情况的分析

Social Media Based Analysis of Opioid Epidemic Using Reddit.

作者信息

Pandrekar Sheetal, Chen Xin, Gopalkrishna Gaurav, Srivastava Avi, Saltz Mary, Saltz Joel, Wang Fusheng

机构信息

Stony Brook University, Stony Brook, NY.

出版信息

AMIA Annu Symp Proc. 2018 Dec 5;2018:867-876. eCollection 2018.

PMID:30815129
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6371364/
Abstract

Opioid-abuse epidemic in the United States has escalated to national attention due to the dramatic increase of opioid overdose deaths. Analyzing opioid-related social media has the potential to reveal patterns of opioid abuse at a national scale, understand opinions of the public, and provide insights to support prevention and treatment. Reddit is a community based social media with more reliable content curated by the community through voting. In this study, we collected and analyzed all opioid related discussions from January 2014 to October 2017, which contains 51,537 posts by 16,162 unique users. We analyzed the data to understand the psychological categories of the posts, and performed topic modeling to reveal the major topics of interest. We also characterized the extent of social support received from comments and scores by each post. Last, we analyzed statistically significant difference in the posts between anonymous and non-anonymous users.

摘要

由于阿片类药物过量死亡人数急剧增加,美国的阿片类药物滥用疫情已引起全国关注。分析与阿片类药物相关的社交媒体有潜力揭示全国范围内阿片类药物滥用的模式,了解公众意见,并为预防和治疗提供支持性见解。Reddit是一个基于社区的社交媒体,其内容由社区通过投票进行更可靠的筛选。在本研究中,我们收集并分析了2014年1月至2017年10月期间所有与阿片类药物相关的讨论,其中包含16162名不同用户发布的51537篇帖子。我们分析数据以了解帖子的心理类别,并进行主题建模以揭示主要关注主题。我们还通过每条帖子的评论和评分来描述所获得的社会支持程度。最后,我们分析了匿名用户和非匿名用户帖子之间的统计学显著差异。