Social media plays a pivotal role in disseminating news across the globe and acts as a platform for people to express their opinions on a variety of topics. COVID-19 vaccination drives across the globe are accompanied by a wide variety of expressed opinions , often colored by emotions . We extracted a corpus of Twitter posts related to COVID-19 vaccination and created two classes of lexical categories- Emotions and Influencing factors. Using unsupervised word embeddings, we tracked the longitudinal change in the latent space of the lexical categories in five countries with strong vaccine roll-out programs, i.e. India, USA , Brazil, UK , and Australia. Nearly 600 thousand vaccine-related tweets from the United States and India were analyzed for an overall understanding of the situation around the world for the time period of 8 months from June 2020 to January 2021. Cosine distance between lexical categories was used to create similarity networks and modules using community detection algorithms . We demonstrate that negative emotions like hesitancy towards vaccines have a high correlation with health-related effects and misinformation. These associations formed a major module with the highest importance in the network formed for January 2021, when millions of vaccines were administered . The relationship between emotions and influencing factors were found to be variable across the countries . By extracting and visualizing these, we propose that such a framework may be helpful in guiding the design of effective vaccine campaigns and can be used by policymakers for modeling vaccine uptake .
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. This study aims at analyzing the temporal evolution of different Emotion categories: Hesitation, Rage, Sorrow, Anticipation, Faith, and Contentment with Influencing Factors: Vaccine Rollout, Misinformation, Health Effects, and Inequities as lexical categories created from Tweets belonging to five countries with vital vaccine roll-out programs, namely, India, United States of America , Brazil, United Kingdom , and Australia. We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination. Using cosine distance from selected seed words, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021. We used community detection algorithms to find modules in positive correlation networks. Our findings suggest that tweets expressing hesitancy towards vaccines contain the highest mentions of health-related effects in all countries. Our results indicated that the patterns of hesitancy were variable across geographies and can help us learn targeted interventions. 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. They formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases . The relationship between Emotions and Influencing Factors was found to be variable across the countries .
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a variety of topics.
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are accompanied by a wide variety of expressed opinions
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. We extracted a corpus of Twitter posts related to COVID-19 vaccination and created two classes of lexical categories- Emotions and Influencing factors. Using unsupervised word embeddings, we tracked the longitudinal change in the latent space of the lexical categories in
, which change along with rising cases, approval of vaccines, and multiple factors discussed online. This study aims at analyzing the temporal evolution of different Emotion categories: Hesitation, Rage, Sorrow, Anticipation, Faith, and Contentment with Influencing Factors: Vaccine Rollout, Misinformation, Health Effects, and Inequities as lexical categories created from Tweets belonging to
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i.e. India, USA
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UK
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Nearly 600 thousand vaccine-related tweets from the United States and India were analyzed for an overall understanding of the situation around the world for the time period of 8 months
We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination. Using cosine distance from selected seed words, we expanded the vocabulary of each category and tracked the longitudinal change in their strength
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January
April
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Cosine distance between lexical categories was used to create similarity networks and modules using
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. We demonstrate that negative emotions like
to find modules in positive correlation networks. Our findings suggest that tweets expressing
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have a high correlation with
contain the highest mentions of
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and misinformation. These associations formed a major module with the
in all countries. Our results indicated that the patterns of hesitancy were variable across geographies and can help us learn targeted interventions. 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
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network formed for January
alluvial diagram. They formed a significant module with all the influencing factors in April
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millions of vaccines were administered
India observed the second wave of COVID-19 cases
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emotions and influencing factors were
Emotions and Influencing Factors was
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. By extracting and visualizing these, we propose that such a framework may be helpful in guiding the design of effective vaccine campaigns and can be used by policymakers for modeling vaccine uptake