Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning

doi:10.1080/07421222.2021.1870388

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning
	Xie, Jiaheng 1; Zhang, Zhu2,4 ; Liu, Xiao 5; Zeng, Daniel2,3,4
发表期刊	JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
ISSN	0742-1222
	2021-01-02
卷号	38 期号:1 页码:166-195
通讯作者	Xie, Jiaheng(jxie@udel.edu)
摘要	Opioid use disorder (OUD) is an epidemic that costs the U.S. healthcare systems $504 billion annually and poses grave mortality risks. Existing studies investigated OUD treatment barriers via surveys as a means to mitigate this opioid crisis. However, the response rate of these surveys is low due to social stigma around opioids. We explore user-generated content in social media as a new data source to study OUD. We design a novel IT system, SImilarity Network-based DEep Learning (SINDEL), to discover OUD treatment barriers from patient narratives and address the challenge of morphs. SINDEL significantly outperforms state-of-the-art NLP models, reaching an F1 score of 76.79 percent. Thirteen types of treatment barriers were identified and verified by domain experts. This work contributes to information systems with a novel deep-learning-based approach for text analytics and generalized design principles for social media analytics methods. We also unveil the hurdles patients endure during the opioid epidemic.
关键词	Computational design science deep learning social media analytics health IT HealthTech opioid addiction addiction treatment
DOI	10.1080/07421222.2021.1870388
收录类别	SCI
语种	英语
资助项目	Ministry of Science and Technology of China[2020AAA0108401] ; Ministry of Science and Technology of China[2017YFC0820105] ; Ministry of Science and Technology of China[2019QY(Y)0101] ; Ministry of Science and Technology of China[2020AAA0103405] ; Ministry of Health of China[2017ZX10303401-002] ; National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[72074209] ; National Natural Science Foundation of China[71974187] ; National Natural Science Foundation of China[71472175] ; National Science Foundation[1228509]
项目资助者	Ministry of Science and Technology of China ; Ministry of Health of China ; National Natural Science Foundation of China ; National Science Foundation
WOS研究方向	Computer Science ; Information Science & Library Science ; Business & Economics
WOS类目	Computer Science, Information Systems ; Information Science & Library Science ; Management
WOS记录号	WOS:000636054300008
出版者	ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44188
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Xie, Jiaheng
作者单位	1.Univ Delaware, Lerner Coll Business & Econ, Dept Accounting & MIS, Newark, DE USA 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Shenzhen Artificial Intelligence & Data Sci Res I, Shenzhen, Guangdong, Peoples R China 5.Arizona State Univ, Dept Informat Syst, Tempe, AZ USA
推荐引用方式 GB/T 7714	Xie, Jiaheng,Zhang, Zhu,Liu, Xiao,et al. Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning[J]. JOURNAL OF MANAGEMENT INFORMATION SYSTEMS,2021,38(1):166-195.
APA	Xie, Jiaheng,Zhang, Zhu,Liu, Xiao,&Zeng, Daniel.(2021).Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning.JOURNAL OF MANAGEMENT INFORMATION SYSTEMS,38(1),166-195.
MLA	Xie, Jiaheng,et al."Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning".JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 38.1(2021):166-195.