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.
J Med Internet Res. 2022 Mar 3;24(3):e24787. doi: 10.2196/24787.
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.
We seek to evaluate whether publicly available near real-time social media images might be used to monitor COVID-19 health policy adherence.
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.
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).
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.
需要创新的监测方法来评估对 COVID-19 建议的遵守情况，特别是能够提供近乎实时或高度具有地域针对性的数据的方法。使用基于位置的社交媒体图像数据（例如 Instagram 图像）是一种可以探索解决此问题的可能方法。
我们旨在评估公开的近乎实时社交媒体图像是否可用于监测 COVID-19 卫生政策的遵守情况。
我们于 2020 年 2 月 7 日至 4 月 11 日从纽约收集了来自以下位置标签的 43487 张 Instagram 图像样本：#中央公园（n=20937）、#布鲁克林大桥（n=14875）和#时代广场（n=7675）。在手动审查图像以确保准确性后，我们计算并记录了每个标签位置的有效每日帖子的频率，以及对这些位置标签处的图像中的个体是否进行社交隔离（即图像中的个体是否彼此之间保持距离，而不是彼此相邻或接触）进行评估和计数。我们分析了随时间发布的图像数量以及标签位置之间趋势的相关性。
我们发现所有区域的帖子数量随时间呈统计上显著下降，每个地点的帖子数量大约下降了 17%（P<.001）。我们发现标签之间存在正相关（#中央公园和#布鲁克林大桥：r=0.40；#布鲁克林大桥和#时代广场：r=0.41；以及#时代广场和#中央公园：r=0.33；所有相关系数均 P<.001）。逻辑回归分析显示，中央公园（P=.004）和布鲁克林大桥（P=.02）上人们似乎保持社交距离的帖子比例随时间略有统计上显著增加，但时代广场（P=.16）则不然。
结果表明，使用基于位置的社交媒体图像数据作为监测 COVID-19 卫生政策遵守情况的方法具有潜力。未来的研究应进一步探讨与该方法相关的实施和道德问题。
