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Public Sentiments & COVID-19 Vaccine

Public Sentiments & COVID-19 Vaccine

Considering Public Sentiments in Vaccine Communication: A Machine Learning Framework

The project resources (including the dataset) can be found in our GitHub repository.

As the world gears up toward recovering from COVID-19, a sustainable clinical and socioeconomic recovery hinges on the development and uptake of an effective vaccine. But studies have indicated an alarming rate of vaccine hesitancy. A global survey has shown that a sizable population of Asia-Pacific may refuse COVID-19 vaccine even if it is proven safe and effective [2], e.g., Russia (55%), Singapore (32%), and the U.S (25%). In Australia, a COVID-19 vaccine refusal rate of at least 10-15% has been estimated, which is enough to significantly undermine the chances of COVID-19 herd immunity. Fears over safety and side effects of these rapidly developed vaccines are major contributors to COVID-19 vaccine hesitancy, and unfortunate incidents like the UQ & UCL vaccine fail can fuel such fears. Worse, such incidents have been exploited by anti-vaccination groups to emotionally manipulate and misinform the public, exacerbating vaccine hesitancy. Hence, as recommended by WHO guidelines, effective vaccine communication is needed to inform and ‘psychologically inoculate’ the population to ensure adequate uptake of the vaccine. Such communication can enhance trust and mitigate fear, and also foster feelings of social and family responsibility, to encourage vaccine acceptance. To achieve this, we need to better characterize the emotional responses that vaccine messages elicit across various socio-economic groups. This information can then be used to optimize strategies for effective targeted COVID-19 vaccine communication.

This project will develop a machine learning framework that analyses public sentiment towards COVID-19 vaccine communication. Outputs will optimize communication strategies, maximizing the vaccine uptake that is critical for socioeconomic recovery in the Asia-Pacific region and beyond. The framework uses advances in Machine Learning and Data Science: sentiment analysis of public emotions such as fear, trust, and feelings of social responsibility will be used to develop effective pro- vaccination communication strategies in news and social media, reducing fear, countering misinformation, and fostering confidence in vaccination.

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