Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data.

Andrew T Lian,Lu Tang,Jingcheng Du

doi:10.3390/vaccines10010103

Andrew T Lian, Lu Tang + Show 1 more

Open Access

https://doi.org/10.3390/vaccines10010103

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Abstract

Social media can be used to monitor the adverse effects of vaccines. The goal of this project is to develop a machine learning and natural language processing approach to identify COVID-19 vaccine adverse events (VAE) from Twitter data. Based on COVID-19 vaccine-related tweets (1 December 2020–1 August 2021), we built a machine learning-based pipeline to identify tweets containing personal experiences with COVID-19 vaccinations and to extract and normalize VAE-related entities, including dose(s); vaccine types (Pfizer, Moderna, and Johnson & Johnson); and symptom(s) from tweets. We further analyzed the extracted VAE data based on the location, time, and frequency. We found that the four most populous states (California, Texas, Florida, and New York) in the US witnessed the most VAE discussions on Twitter. The frequency of Twitter discussions of VAE coincided with the progress of the COVID-19 vaccinations. Sore to touch, fatigue, and headache are the three most common adverse effects of all three COVID-19 vaccines in the US. Our findings demonstrate the feasibility of using social media data to monitor VAEs. To the best of our knowledge, this is the first study to identify COVID-19 vaccine adverse event signals from social media. It can be an excellent supplement to the existing vaccine pharmacovigilance systems.

Highlights

As of December 2021, the COVID-19 pandemic has claimed over five million lives worldwide [1]
Our findings demonstrate the feasibility of using social media data to monitor vaccine adverse events (VAE)
In addition to the traditional reporting channels established by governments and pharmaceutical companies such as Vaccine Adverse Event Reporting System (VAERS), social media provides an opportunity for the surveillance of vaccine adverse events (VAEs), as social media users are likely to discuss their vaccination experiences on social media

Summary

Introduction

As of December 2021, the COVID-19 pandemic has claimed over five million lives worldwide [1]. The COVID-19 vaccines have been proven to reduce infections, serious illness, hospitalizations, and dearth [2]. As new variants such as Delta and Omicron have emerged, the efficacy of the vaccines declined, but two shots and one booster still provide some protection again infection and solid protection against severe illness, hospitalization, and death [3,4]. As different COVID-19 vaccines have been distributed to billions of individuals worldwide, it is critical to continue to monitor the safety signals of vaccines and to track rare events. Health authorities use both active surveillance such as Sentinel BEST (Biologics Effectiveness and Safety) and passive surveillance such as the Vaccine Adverse Event Reporting System (VAERS) to collect and share information about adverse events [5]. In addition to the traditional reporting channels established by governments and pharmaceutical companies such as VAERS, social media provides an opportunity for the surveillance of vaccine adverse events (VAEs), as social media users are likely to discuss their vaccination experiences on social media

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