JMIR Publications

ABSTRACT

Background:

Amid the global COVID-19 pandemic a worldwide infodemic also emerged with large amounts of COVID-19 related information and misinformation spreading through social media channels. Various organizations including the World Health Organization and Centers for Disease Control and Prevention and other prominent individuals issued high profile advice on preventing the further spread of COVID-19.

Objective:

The purpose of this study was to leverage machine learning and Twitter data during the pandemic to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action.

Methods:

A total of 646,885,238 COVID-19 English-language related tweets were filtered creating a mask-wearing dataset and a vaccine dataset. Researchers manually categorized a training sample of 3500 tweets for each dataset according to their relevance to health belief model constructs and used coded tweets to train machine learning models for classifying each tweet in the datasets.

Results:

Five models were trained, respectively, for both the mask-related dataset and vaccine-related dataset using the XLNet transformer model with each model achieving at least 81% classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask-wearing and immunizations; however, the strength of these beliefs appeared to vary in response to high profile cues to action.

Conclusions:

During both the COVID-19 pandemic and infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high profile cues to action from prominent organizations and individuals.