Background: The coronavirus disease (COVID-19) pandemic has caused a significant burden of mortality and morbidity. A vaccine will be the most effective global preventive strategy to end the pandemic. Studies have maintained that exposure to negative sentiments related to vaccination on social media increase vaccine hesitancy and refusal. Despite the influence social media has on vaccination behavior, there is a lack of studies exploring the public's exposure to misinformation, conspiracy theories, and concerns on Twitter regarding a potential COVID-19 vaccination. Objective: The study aims to identify the major thematic areas about a potential COVID-19 vaccination based on the contents of Twitter data. Method: We retrieved 1,286,659 publicly available tweets posted within the timeline of July 19, 2020, to August 19, 2020, leveraging the Twint package. Following the extraction, we used Latent Dirichlet Allocation for topic modelling and identified 20 topics discussed in the tweets. We selected 4,868 tweets with the highest probability of belonging in the specific cluster and manually labeled as positive, negative, neutral, or irrelevant. The negative tweets were further assigned to a theme and subtheme based on the contentResult: The negative tweets were further categorized into 7 major themes: "safety and effectiveness,” "misinformation,” "conspiracy theories,” "mistrust of scientists and governments,” "lack of intent to get a COVID-19 vaccine,” "freedom of choice," and "religious beliefs. Negative tweets predominantly consisted of misleading statements (n=424) that immunization against coronavirus is unnecessary as the survival rate is high. The second most prevalent theme to emerge was tweets constituting safety and effectiveness related concerns (n=276) regarding the side effects of a potential vaccine developed at an unprecedented speed. Conclusion: Our findings suggest a need to formulate a large-scale vaccine communication plan that will address the safety concerns and debunk the misinformation and conspiracy theories spreading across social media platforms, increasing the public's acceptance of a COVID-19 vaccination.

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Opportunities to answer many real life queries such as "which surveillance camera has the best view of a moving car in the presence of obstacles?" have become a reality due to the development of location based services and recent advances in 3D modeling of urban environments. In this paper, we investigate the problem of continuously finding the best viewpoint from a set of candidate viewpoints that provides the best view of a moving target in presence of visual obstacles in 2D or 3D space. We propose a query type called k Continuous Maximum Visibility (kCMV) query that ranks k query viewpoints (or locations) from a set of candidate viewpoints in the increasing order of the visibility measure of the target from these viewpoints. We propose two approaches that reduce the set of query locations and obstacles to consider during visibility computation and efficiently update the results as target moves. We conduct extensive experiments to demonstrate the effectiveness and efficiency of our solutions for a moving target in presence of obstacles.

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