Analysis of Public Sentiment on COVID-19 Mitigation Measures in Social Media in the United States Using Machine Learning

ABSTRACT

Public sentiment can impact the implementation of public policies and even cause policy failure if public support is not received. Therefore, knowledge of public sentiment concerning new and emerging policies is critical for policymakers. During the coronavirus disease 2019 (COVID-19) pandemic, several precautionary measures have been suggested in an attempt to delay or mitigate the spread of the virus. This study presents a framework that applies natural language processing (NLP) techniques, such as sentiment and bigram analyses, to characterize the public sentiment on three prominent mitigation measures (mask wearing, social distancing, and quarantine) as shared by Twitter users in the United States. As part of the framework, we apply a bigram graph-based approach to visualize the most frequent topics in Twitter discussions during the COVID-19 pandemic. The objective is to provide insights into the most commonly discussed topics among Twitter users with similar demographic characteristics (e.g., age and gender). The sentiment and bigram analyses identified the most frequently discussed topics expressing both positive and negative sentiments among different age and gender groups. Discussions containing positive sentiment prevailed and revolved around the benefits of the measures and trust in the government, while the topics of negative sentiment involved conspiracy theories, skepticism, and distrust of government mandates. It is also notable that the discussions among people 19–29 and over 40 years old focus on government officials and political parties, benefits or inefficiency of mitigation measures, and conspiracy theories more often than other demographic groups. Our proposed approaches and results offer a novel and potentially valuable contribution to public policymakers. IEEE