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

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.

A Machine Learning Approach to Predict the Rehabilitation Outcome in Convalescent COVID-19 Patients

“…This model exhibited a similar performance in both internal (AUROC: 86.0%) and external validation (AUROC: 86.0%) [ 34 ]. Several other studies used ML to predict mortality [ 35 ] but also to evaluate the necessity of oxygen supplementation [ 36 ], to monitor pandemic-related psychopathology [ 37 ], to identify vaccine-related adverse events from Twitter data [ 38 ], and even to diagnose COVID-19 from cough audio signals [ 39 ]. However, to the best of our knowledge, no previous study used ML to predict the rehabilitation outcome in the post-acute phase of COVID-19.…”

Pharmacovigilance and Pharmacoepidemiology as a Guarantee of Patient Safety: The Role of the Clinical Pharmacologist

“…In recent years, new technologies are also making an important contribution to active pharmacovigilance. In particular, machine learning, deep learning, and natural language processing approaches can be used to detect adverse reactions from unconventional data sources, e.g., social media [ 16 , 17 ], and to discover safety signals, underlying some adverse reactions not yet reported [ 18 ]. Moreover, safety data collected from healthcare social networks and forums, general social networking, and search logs can be processed by big data sentiment analysis algorithms to provide a more comprehensive picture of the public opinion, experiences, and sentiments about drugs and vaccines [ 19 ], and can be used to create effective public health campaigns on drugs/vaccines safety, diseases prevention or to fight vaccine hesitancy [ 20 ].…”

Comparing Worldwide, National, and Independent Notifications about Adverse Drug Reactions Due to COVID-19 Vaccines

“…Lian et al [17] implemented a machine-learning-based pipeline to identify tweets containing personal experiences with COVID-19 vaccinations in the US, showing that pain to the touch, fatigue, and headache were the three most commonly reported adverse effects. In our case, the adverse events data are already available, so it is not required to find them; however, the method proposed in [17] cover only the step data analysis of our methodology and may be considered as alternative technique to detect possible safety problems in licensed vaccines.…”