Cuomo Raphael E, Purushothaman Vidya, Li Jiawei, Cai Mingxiang, Mackey Tim K
Department of Anesthesiology, University of California, San Diego School of Medicine, San Diego, CA, USA.
Global Health Policy and Data Institute, San Diego, CA, USA.
BMC Public Health. 2021 Apr 24;21(1):793. doi: 10.1186/s12889-021-10827-4.
Early reports of COVID-19 cases and deaths may not accurately convey community-level concern about the pandemic during early stages, particularly in the United States where testing capacity was initially limited. Social media interaction may elucidate public reaction and communication dynamics about COVID-19 in this critical period, during which communities may have formulated initial conceptions about the perceived severity of the pandemic.
Tweets were collected from the Twitter public API stream filtered for keywords related to COVID-19. Using a pre-existing training set, a support vector machine (SVM) classifier was used to obtain a larger set of geocoded tweets with characteristics of user self-reporting COVID-19 symptoms, concerns, and experiences. We then assessed the longitudinal relationship between identified tweets and the number of officially reported COVID-19 cases using linear and exponential regression at the U.S. county level. Changes in tweets that included geospatial clustering were also assessed for the top five most populous U.S. cities.
From an initial dataset of 60 million tweets, we analyzed 459,937 tweets that contained COVID-19-related keywords that were also geolocated to U.S. counties. We observed an increasing number of tweets throughout the study period, although there was variation between city centers and residential areas. Tweets identified as COVID-19 symptoms or concerns appeared to be more predictive of active COVID-19 cases as temporal distance increased.
Results from this study suggest that social media communication dynamics during the early stages of a global pandemic may exhibit a number of geospatial-specific variations among different communities and that targeted pandemic communication is warranted. User engagement on COVID-19 topics may also be predictive of future confirmed case counts, though further studies to validate these findings are needed.
关于新冠疫情病例和死亡的早期报告可能无法准确传达疫情早期社区层面的担忧，尤其是在美国，最初检测能力有限。社交媒体互动可能有助于阐明这一关键时期公众对新冠疫情的反应和传播动态，在此期间，社区可能已对疫情的严重程度形成了初步认知。
从推特公共应用程序编程接口（API）流中收集与新冠疫情相关关键词过滤后的推文。使用预先存在的训练集，支持向量机（SVM）分类器用于获取更多具有用户自我报告新冠症状、担忧和经历特征的地理编码推文。然后，我们在美国县级层面使用线性和指数回归评估已识别推文与官方报告的新冠病例数之间的纵向关系。还对美国人口最多的五个城市中包含地理空间聚类的推文变化进行了评估。
从最初的6000万条推文数据集中，我们分析了459,937条包含新冠相关关键词且地理位置在美国各县的推文。在整个研究期间，我们观察到推文数量不断增加，尽管市中心和居民区之间存在差异。随着时间距离增加，被识别为新冠症状或担忧的推文似乎对活跃的新冠病例更具预测性。
本研究结果表明，全球大流行早期阶段的社交媒体传播动态可能在不同社区之间呈现出一些地理空间特定的差异，有必要进行有针对性的疫情传播。用户对新冠主题的参与度也可能预测未来确诊病例数，不过需要进一步研究来验证这些发现。
