JMIR Publications

ABSTRACT

Background:

In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be timely investigated to minimize harm in the population. If not properly dealt with, side effects may also impact the public trust in the vaccination campaigns carried out by the national governments.

Objective:

Monitoring social media for the early identification of side effects and understanding the public opinion on the vaccines are of paramount importance to ensure a successful and harmless rollout. The objective is to create a web portal to monitor the opinion of social media users on the vaccines, to provide a tool for journalists, scientists, and users alike to visualize how the general public is reacting to the vaccination campaign.

Methods:

In this paper, we present a tool to analyze the public opinion on COVID-19 vaccines from Twitter, exploiting, among the others: a state-of-the-art system for the identification of Adverse Drug Events (ADEs) on social media; Natural Language Processing models for sentiment analysis; statistical tools and open-source databases to visualize the trending hashtags, news articles and their factuality. All the modules of the system are displayed through a web portal available at http://ailab.uniud.it/covid-vaccines/.

Results:

A set of 650,000 tweets have been collected and analyzed starting from December 2020. Tweet collection and analysis is an ongoing process. The results of the analysis are made public on a web portal (updated daily), together with a description of the processing methods and ways to access the preprocessed data. The collected data provide sensible insights in the public opinion on the vaccines and how their main worries changed in time. They show how news coverage had a high impact on the set of topics discussed by Twitter users, and the reassuring trend that users have a high tendency to only share news from reliable sources when discussing COVID-19 vaccines.

Conclusions:

We presented a tool connected with a web portal to monitor and display some key aspects of the public's reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations and represents a tool for the extraction of suspected adverse events from tweets with a Deep Learning model.