Accepted for/Published in: JMIR Infodemiology
Date Submitted: Oct 17, 2021
Date Accepted: Mar 31, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Mining Trends of COVID-19 Vaccine Beliefs on Twitter with Lexical Embeddings
ABSTRACT
Background:
Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompanies COVID-19 vaccination drives across the globe, often colored by emotions, which change along with rising cases, approval of vaccines, and multiple factors discussed online.
Objective:
This study aims at analyzing the temporal evolution of different emotions and the related influencing factors in tweets belonging to five countries with vital vaccine roll-out programs, namely, India, United States of America(USA), Brazil, United Kingdom(UK), and Australia.
Methods:
We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created two classes of lexical categories – Emotions and Influencing factors. Using cosine distance from selected seed words’ embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks.
Results:
Our findings indicated the varying relationship among Emotions and Influencing Factors across countries. Tweets expressing hesitancy towards vaccines contained the highest mentions of health-related effects in all countries. We also observed a significant change in the linear trends of categories like hesitation and contentment before and after approval of vaccines. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases.
Conclusions:
By extracting and visualizing these, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policymakers to model vaccine uptake and targeted interventions.
Citation
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Copyright
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