论文标题
在线集体关注Covid-19大流行的不同模式与未来的案件差异有关
Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance
论文作者
论文摘要
使用从2019-09-01到2020-04-30撰写的随机10％的推文样本，我们分析了Twitter上使用的单词（1克）的动态行为，以描述正在进行的COVID-19-19-19大流行。在24种语言中，我们发现了两个独特的动态政权：一种表征了1月下旬对最初的冠状病毒爆发的集体关注时的上升和随后的崩溃，第二个代表了Covid-19与Covid-19与COVID相关的话语。通过主导语言使用汇总国家，我们发现第一个动态制度的波动率大约三周（平均22.49 $ \ pm $ 3.26天）与未来的波动率有关。我们的结果表明，社交媒体上与流行病学相关词的使用变化的监视可能有助于预测以后的疾病病例数的变化，但我们强调，我们目前的发现不是因果关系或必要的预测性。
Using a random 10% sample of tweets authored from 2019-09-01 through 2020-04-30, we analyze the dynamic behavior of words (1-grams) used on Twitter to describe the ongoing COVID-19 pandemic. Across 24 languages, we find two distinct dynamic regimes: One characterizing the rise and subsequent collapse in collective attention to the initial Coronavirus outbreak in late January, and a second that represents March COVID-19-related discourse. Aggregating countries by dominant language use, we find that volatility in the first dynamic regime is associated with future volatility in new cases of COVID-19 roughly three weeks (average 22.49 $\pm$ 3.26 days) later. Our results suggest that surveillance of change in usage of epidemiology-related words on social media may be useful in forecasting later change in disease case numbers, but we emphasize that our current findings are not causal or necessarily predictive.
