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.

中文翻译：

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对 COVID-19 大流行的在线集体关注的不同模式与未来的病例量差异有关

使用从 2019 年 9 月 1 日到 2020 年 4 月 30 日创作的随机 10% 推文样本，我们分析了 Twitter 上用来描述正在进行的 COVID-19 大流行的单词（1 克）的动态行为。在 24 种语言中，我们发现了两种截然不同的动态机制：一种表征了对 1 月下旬初始冠状病毒爆发的集体注意力的上升和随后的崩溃，另一种代表了 3 月与 COVID-19 相关的话语。按主要语言使用汇总国家，我们发现第一个动态机制的波动性与大约三周后（平均 22.49 $\pm$ 3.26 天）的 COVID-19 新病例的未来波动性相关。我们的结果表明，对社交媒体上流行病学相关词汇使用变化的监测可能有助于预测疾病病例数的后期变化，