Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

Abstract

Pandemics and infectious diseases are overcome by vaccination, which serves as a preventative measure. Nevertheless, vaccines also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines. COVID-19 vaccines received a similarly hostile reaction from the public. In addition, misinformation from social media, contradictory comments from medical experts, and reports of worse reactions led to negative COVID-19 vaccine perceptions. Many researchers analyzed people’s varying sentiments regarding the COVID-19 vaccine using artificial intelligence (AI) approaches. This study is the first attempt to review the role of AI approaches in COVID-19 vaccination-related sentiment analysis. For this purpose, insights from publications are gathered that analyze the (a) approaches used to develop sentiment analysis tools, (b) major sources of data, (c) available data sources, and (d) the public perception of COVID-19 vaccine. Analysis suggests that public perception-related COVID-19 tweets are predominantly analyzed using TextBlob. Moreover, to a large extent, researchers have employed the Latent Dirichlet Allocation model for topic modeling of Twitter data. Another pertinent discovery made in our study is the variation in people’s sentiments regarding the COVID-19 vaccine across different regions. We anticipate that our systematic review will serve as an all-in-one source for the research community in determining the right technique and data source for their requirements. Our findings also provide insight into the research community to assist them in their future work in the current domain.

Generative artificial intelligence can have a role in combating vaccine hesitancy

“… 9 Generative AI methods such as sentiment tracking and topic modelling can interpret and generate content, including text and images, and resolve complex data analysis challenges rapidly. 10 They allow real time understanding of hesitancy topics and trends, 11 which is essential to inform interventions such as data driven chatbots that provide or contextualise health information. 12 …”

“…9 Generative AI methods such as sentiment tracking and topic modelling can interpret and generate content, including text and images, and resolve complex data analysis challenges rapidly. 10 They allow real time understanding of hesitancy topics and trends, 11 which is essential to inform interventions such as data driven chatbots that provide or contextualise health information. 12 Nevertheless, such tools, if not employed judiciously, have the potential to generate misinformation intentionally or unintentionally by referencing inaccurate information, influencing public opinion, and discouraging vaccination.…”

Deep Learning-Based Sentiment and Stance Analysis of Tweets About Vaccination

“…A recent survey of artificial intelligence studies for sentiment analysis on tweets about COVID-19 vaccines is presented in (Aljedaani et al, 2022). This survey spans a period of two years (2020 and 2021) and suggests that publicly available sentiment analysis tools are predominantly used in the related studies and that public sentiment towards the vaccines varies across different regions of the world.…”

Use of AI Applications to Learn the Sentiment Polarity of Public Perceptions: A Case Study of the COVID-19 Vaccinations in the UAE

“…As such, we need to devise methods that will allow us to analyze and interpret the posts that users make and also to determine what a given perception of a given vaccine is. This paper deals with an extensive analysis of the data that we have collected within the small duration the vaccination process in order to analyze the public perception of a given vaccine [3]. The research begins with the collection of tweets from various uses that will allow us to get an idea of what the users think about the vaccines themselves.…”