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

Autor: Young SD; Department of Informatics, University of California Institute for Prediction Technology, University of California, Irvine, Irvine, CA, United States.; Department of Emergency Medicine, University of California, Irvine, Irvine, CA, United States., Zhang Q; School of Data Science, City University of Hong Kong, Kowloon, Hong Kong, Hong Kong., Zeng DD; Institute of Automation, Chinese Academy of Sciences, Beijing, China., Zhan Y; Department of Information Systems, California Polytechnic State University, San Luis Obispo, CA, United States., Cumberland W; Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States.
Jazyk: angličtina
Zdroj: Journal of medical Internet research [J Med Internet Res] 2022 Mar 03; Vol. 24 (3), pp. e24787. Date of Electronic Publication: 2022 Mar 03.
DOI: 10.2196/24787
Abstrakt: 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.
(©Sean D Young, Qingpeng Zhang, Daniel Dajun Zeng, Yongcheng Zhan, William Cumberland. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.03.2022.)
Databáze: MEDLINE
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