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Social Media Images as an Emerging Tool to Monitor Adherence to COVID-19 Public Health Guidelines: Content Analysis

文献类型：期刊论文

作者Young, Sean D.3,4; Zhang, Qingpeng2; Zeng, Daniel Dajun1; Zhan, Yongcheng6; Cumberland, William5
刊名JOURNAL OF MEDICAL INTERNET RESEARCH
出版日期2022-03-01
卷号24期号:3页码:5
ISSN号1438-8871
关键词internet social media health informatics tool monitor adherence COVID-19 public health guidelines content analysis policy
DOI10.2196/24787
通讯作者Young, Sean D.(syoung5@hs.uci.edu)
英文摘要Background: Innovative surveillance methods are needed to assess adherence to COVID-19 recommendations, especially methods that can provide near real-time or highly geographically targeted data. Use of location-based social media image data (eg, Instagram images) is one possible approach that could be explored to address this problem. Objective: We seek to evaluate whether publicly available near real-time social media images might be used to monitor COVID-19 health policy adherence. Methods: We collected a sample of 43,487 Instagram images in New York from February 7 to April 11, 2020, from the following location hashtags: #Centralpark (n=20,937), #Brooklyn Bridge (n=14,875), and #Timesquare (n=7675). After manually reviewing images for accuracy, we counted and recorded the frequency of valid daily posts at each of these hashtag locations over time, as well as rated and counted whether the individuals in the pictures at these location hashtags were social distancing (ie, whether the individuals in the images appeared to be distanced from others vs next to or touching each other). We analyzed the number of images posted over time and the correlation between trends among hashtag locations. Results: We found a statistically significant decline in the number of posts over time across all regions, with an approximate decline of 17% across each site (P<.001). We found a positive correlation between hashtags (#Centralpark and #Brooklynbridge: r=0.40; #BrooklynBridge and #Timesquare: r=0.41; and #Timesquare and #Centralpark: r=0.33; P<.001 for all correlations). The logistic regression analysis showed a mild statistically significant increase in the proportion of posts over time with people appearing to be social distancing at Central Park (P=.004) and Brooklyn Bridge (P=.02) but not for Times Square (P=.16). Conclusions: Results suggest the potential of using location-based social media image data as a method for surveillance of COVID-19 health policy adherence. Future studies should further explore the implementation and ethical issues associated with this approach.
资助项目National Institute of Allergy and Infectious Diseases (NIAID) ; National Institute on Drug Abuse (NIDA)
WOS研究方向Health Care Sciences & Services ; Medical Informatics
语种英语
出版者JMIR PUBLICATIONS, INC
WOS记录号WOS:000790206800001
资助机构National Institute of Allergy and Infectious Diseases (NIAID) ; National Institute on Drug Abuse (NIDA)
源URL[http://ir.ia.ac.cn/handle/173211/48409]  
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Young, Sean D.
作者单位1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.City Univ Hong Kong, Sch Data Sci, Kowloon, Hong Kong, Peoples R China
3.Univ Calif Irvine, Univ Calif Inst Predict Technol, Dept Informat, 6091 Bren Hall, Irvine, CA 92617 USA
4.Univ Calif Irvine, Dept Emergency Med, Irvine, CA USA
5.Univ Calif Los Angeles, Dept Biostat, Los Angeles, CA USA
6.Calif Polytech State Univ San Luis Obispo, Dept Informat Syst, San Luis Obispo, CA USA
推荐引用方式
GB/T 7714		 Young, Sean D.,Zhang, Qingpeng,Zeng, Daniel Dajun,et al. Social Media Images as an Emerging Tool to Monitor Adherence to COVID-19 Public Health Guidelines: Content Analysis[J]. JOURNAL OF MEDICAL INTERNET RESEARCH,2022,24(3):5.
APA Young, Sean D.,Zhang, Qingpeng,Zeng, Daniel Dajun,Zhan, Yongcheng,&Cumberland, William.(2022).Social Media Images as an Emerging Tool to Monitor Adherence to COVID-19 Public Health Guidelines: Content Analysis.JOURNAL OF MEDICAL INTERNET RESEARCH,24(3),5.
MLA Young, Sean D.,et al."Social Media Images as an Emerging Tool to Monitor Adherence to COVID-19 Public Health Guidelines: Content Analysis".JOURNAL OF MEDICAL INTERNET RESEARCH 24.3(2022):5.

入库方式： OAI收割

来源：自动化研究所

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