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

doi:10.1080/07421222.2021.1870388

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning
	Xie, Jiaheng 3; Zhang, Zhu 2,5; Liu, Xiao 4; Zeng, Daniel 1,2,5
刊名	JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
	2021-01-02
卷号	38 期号:1 页码:166-195
关键词	Computational design science deep learning social media analytics health IT HealthTech opioid addiction addiction treatment
ISSN号	0742-1222
DOI	10.1080/07421222.2021.1870388
通讯作者	Xie, Jiaheng(jxie@udel.edu)
英文摘要	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.
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]
WOS研究方向	Computer Science ; Information Science & Library Science ; Business & Economics
语种	英语
出版者	ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
WOS记录号	WOS:000636054300008
资助机构	Ministry of Science and Technology of China ; Ministry of Health of China ; National Natural Science Foundation of China ; National Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44187]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者	Xie, Jiaheng
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 3.Univ Delaware, Lerner Coll Business & Econ, Dept Accounting & MIS, Newark, DE USA 4.Arizona State Univ, Dept Informat Syst, Tempe, AZ USA 5.Shenzhen Artificial Intelligence & Data Sci Res I, Shenzhen, Guangdong, Peoples R China
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.