We study the causal effects of lockdown measures on uncertainty and sentiment on Twitter. To this end, we exploit the quasi-experimental framework created by the first COVID-19 lockdown in a high-income economy--the unexpected Italian lockdown in February 2020. We measure changes in public sentiment using deep learning and dictionary-based methods on the text of daily tweets geolocated within and near the locked-down areas, before and after the treatment. We classify tweets into four categories--economics, health, politics, and lockdown policy--to examine how the policy affected emotions heterogeneously. Using a staggered difference-in-differences approach, we show that the lockdown did not have a significantly robust impact on economic uncertainty and sentiment. However, the policy came at the price of higher uncertainty on health and politics and more negative political sentiments. These results, which are robust to a battery of robustness tests, show that lockdowns have relevant non-health related implications.

翻译：我们研究了封锁措施对Twitter上不确定性和情绪的因果影响。为此，我们利用了高收入经济体首次COVID-19封锁——2020年2月意大利意外封锁——所创造准实验框架。我们采用深度学习和基于词典的方法，对封锁区域内外的每日推文文本进行分析，测量处理前后公众情绪的变化。我们将推文划分为四类——经济、健康、政治和封锁政策——以考察政策如何异质性影响情绪。通过交错双重差分方法，我们表明封锁并未对经济不确定性和情绪产生显著稳健的影响。然而，该政策以健康和政治不确定性升高以及政治情绪更加消极为代价。这些结果经过一系列稳健性检验后依然成立，表明封锁具有相关的非健康影响。