(Un)Masked COVID-19 Trends from Social Media
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COVID-19 has affected the entire world. One useful protection method for people against COVID-19 is to wear masks in public areas. Across the globe, many public service providers have mandated correctly wearing masks to use their services. This paper proposes two new datasets VAriety MAsks - Classification VAMA-C) and VAriety MAsks - Segmentation (VAMA-S), for mask detection and mask fit analysis tasks, respectively. We propose a framework for classifying masked and unmasked faces and a segmentation based model to calculate the mask-fit score. Both the models trained in this study achieved an accuracy of 98 social media images for six major US cities were analyzed. By comparing the regulations, an increase in masks worn in images as the COVID-19 cases rose in these cities was observed, particularly when their respective states imposed strict regulations. Furthermore, mask compliance in the Black Lives Matter protest was analyzed, eliciting that 40 masks, and 45
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