Analyzing public perceptions toward COVID-19 Vaccination process using social media and machine learning

ABSTRACT

Since the declaration of COVID-19 as a global pandemic, the world is disrupting socially and economically. COVID-19 vaccines are still in the human trials and the governments should understand the society attitudes towards the vaccinations acceptance/hesitancy in order to deliver more accurate plans and health messages. Social media represents a catalog of our daily-life communications and activities. In this paper, we utilized the power of social media and machine learning to gain insights and understand the public attitudes towards COVID-19 vaccinations. The peak of online vaccination conversation on a social media happened with the authorization of Moderna and Pfizer vaccines for the emergency usage. The sentiment analysis of the clustered tweets relevant to the vaccination topic reveals that most of the public opinions was neutral and target the understanding of the vaccination process, confirming its efficiency and safety, and the countries plans to distribute and secure doses for their residents. The second top sentiment analysis group has negative attitudes according to spreading claims of the vaccines productions, side effects, and the previously reported vaccinations trails in different historical pandemic periods. The reported analysis raised the emergency need for interactive communications with communities from different cultural and educational level to increase their vaccination awareness and validate the vaccines' associated news. The decision makers' deliverable speech should simplify the scientific terms, target the community fears and release any public concerns regarding the vaccination process and its distribution plans. © 2021 Association for Computing Machinery.