Polyzos Efstathios, Fotiadis Anestis, Huan Tzung-Cheng
College of Business, Zayed University, Abu Dhabi Campus, United Arab Emirates.
Department of Marketing and Tourism Management, National Chiayi University, Taiwan.
Technol Soc. 2023 Feb;72:102198. doi: 10.1016/j.techsoc.2023.102198. Epub 2023 Jan 21.
This paper examines the effects of online campaigns celebrating frontline workers on COVID-19 outcomes regarding new cases, deaths, and vaccinations, using the United Kingdom as a case study. We implement text and sentiment analysis on Twitter data and feed the result into random regression forests and cointegration analysis. Our combined machine learning and econometric approach shows very weak effects of both the volume and the sentiment of Twitter discussions on new cases, deaths, and vaccinations. On the other hand, established relationships (such as between stringency measures and cases/deaths and between vaccinations and deaths) are confirmed. On the contrary, we find adverse lagged effects from negative sentiment to vaccinations and from new cases to negative sentiment posts. As we assess the knowledge acquired from the COVID-19 crisis, our findings can be used by policy makers, particularly in public health, and prepare for the next pandemic.
本文以英国为案例研究，考察了颂扬一线工作者的线上活动对新冠疫情相关的新增病例、死亡人数和疫苗接种情况的影响。我们对推特数据进行文本和情感分析，并将结果输入随机回归森林和协整分析。我们结合机器学习和计量经济学的方法表明，推特讨论的数量和情感对新增病例、死亡人数和疫苗接种的影响非常微弱。另一方面，已确立的关系（如严格措施与病例/死亡人数之间的关系以及疫苗接种与死亡人数之间的关系）得到了证实。相反，我们发现负面情绪对疫苗接种以及新增病例对负面情绪帖子存在不利的滞后影响。在我们评估从新冠疫情危机中获得的知识时，政策制定者，尤其是公共卫生领域的政策制定者，可以利用我们的研究结果为下一次大流行做好准备。
