@Article{info:doi/10.2196/64667,
author="Biliotti, Carolina
and Fraccaroli, Nicol{\`o}
and Puliga, Michelangelo
and Bargagli-Stoffi, Falco J
and Riccaboni, Massimo",
title="The Impact of Stay-At-Home Mandates on Uncertainty and Sentiments: Quasi-Experimental Study",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="4",
volume="27",
pages="e64667",
keywords="lockdown policy; sentiment analysis; uncertainty; social media; quasi-experiment",
abstract="Background: As the spread of the SARS-CoV-2 virus coincided with lockdown measures, it is challenging to distinguish public reactions to lockdowns from responses to COVID-19 itself. Beyond the direct impact on health, lockdowns may have worsened public sentiment toward politics and the economy or even heightened dissatisfaction with health care, imposing a significant cost on both the public and policy makers. Objective: This study aims to analyze the causal effect of COVID-19 lockdown policies on various dimensions of sentiment and uncertainty, using the Italian lockdown of February 2020 as a quasi-experiment. At the time of implementation, communities inside and just outside the lockdown area were equally exposed to COVID-19, enabling a quasi-random distribution of the lockdown. Additionally, both areas had similar socioeconomic and demographic characteristics before the lockdown, suggesting that the delineation of the strict lockdown zone approximates a randomized experiment. This approach allows us to isolate the causal effects of the lockdown on public emotions, distinguishing the impact of the policy itself from changes driven by the virus's spread. Methods: We used Twitter data (N=24,261), natural language models, and a difference-in-differences approach to compare changes in sentiment and uncertainty inside (n=1567) and outside (n=22,694) the lockdown areas before and after the lockdown began. By fine-tuning the AlBERTo (Italian BERT optimized) pretrained model, we analyzed emotions expressed in tweets from 1124 unique users. Additionally, we applied dictionary-based methods to categorize tweets into 4 dimensions---economy, health, politics, and lockdown policy---to assess the corresponding emotional reactions. This approach enabled us to measure the direct impact of local policies on public sentiment using geo-referenced social media and can be easily adapted for other policy impact analyses. Results: Our analysis shows that the lockdown had no significant effect on economic uncertainty (b=0.005, SE 0.007, t125=0.70; P=.48) or negative economic sentiment (b=--0.011, SE 0.0089, t125=--1.32; P=.19). However, it increased uncertainty about health (b=0.036, SE 0.0065, t125=5.55; P<.001) and lockdown policy (b=0.026, SE 0.006, t125=4.47; P<.001), as well as negative sentiment toward politics (b=0.025, SE 0.011, t125=2.33; P=.02), indicating that lockdowns have broad externalities beyond health. Our key findings are confirmed through a series of robustness checks. Conclusions: Our findings reveal that lockdowns have broad externalities extending beyond health. By heightening health concerns and negative political sentiment, policy makers have struggled to secure explicit public support for government measures, which may discourage future leaders from implementing timely stay-at-home policies. These results highlight the need for authorities to leverage such insights to enhance future policies and communication strategies, reducing uncertainty and mitigating social panic. ",
issn="1438-8871",
doi="10.2196/64667",
url="https://www.jmir.org/2025/1/e64667",
url="https://doi.org/10.2196/64667",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053818"
}