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As COVID-19 ravages the world, social media analytics could augment traditional surveys in assessing how the pandemic evolves and capturing consumer chatter that could help healthcare agencies in addressing it. This typically involves mining disclosure events that mention testing positive for the disease or discussions surrounding perceptions and beliefs in preventative or treatment options. The 2020 shared task on COVID-19 event extraction (conducted as part of the W-NUT workshop during the EMNLP conference) introduced a new Twitter dataset for benchmarking event extraction from COVID-19 tweets. In this paper, we cast the problem of event extraction as extractive question answering using recent advances in continuous prompting in language models. On the shared task test dataset, our approach leads to over 5% absolute micro-averaged F1-score improvement over prior best results, across all COVID-19 event slots. Our ablation study shows that continuous prompts have a major impact on the eventual performance. [ABSTRACT FROM AUTHOR]

This poster describes a methodology to numerically represent the happiness of a city by mining user generated terms in Flickr.com. As a pilot analysis, we collected 15,000 text records consisting of titles, tags, descriptions, and comments for the thirty most populous cities in the United States. Parsed text was utilized to calculate happiness scores (H-Score) by matching text extracted from Flickr.com with a happiness index dictionary. In addition, we examined the relationships between the calculated Hscores and real world phenomena including population, crime rate, and climate. Based on this pilot analysis, a future study is planed that involves a large dataset with prediction analysis. [ABSTRACT FROM AUTHOR]