The severity of the coronavirus pandemic necessitates the need of effective administrative decisions. Over 4 lakh people in India succumbed to COVID-19, with over 3 crore confirmed cases, and still counting. The threat of a plausible third wave continues to haunt millions. In this ever changing dynamic of the virus, predictive modeling methods can serve as an integral tool. The pandemic has further triggered an unprecedented usage of social media. This paper aims to propose a method for harnessing social media, specifically Twitter, to predict the upcoming scenarios related to COVID-19 cases. In this study, we seek to understand how the surges in COVID-19 related tweets can indicate rise in the cases. This prospective analysis can be utilised to aid administrators about timely resource allocation to lessen the severity of the damage. Using word embeddings to capture the semantic meaning of tweets, we identify Significant Dimensions (SDs).Our methodology predicts the rise in cases with a lead time of 15 days and 30 days with R2 scores of 0.80 and 0.62 respectively. Finally, we explain the thematic utility of the SDs.
翻译:科罗纳病毒大流行的严重性要求做出有效的行政决定。在印度,超过4 000人死于COVID-19, 超过3个已证实病例,仍在数数。第三波的威胁继续困扰着数百万人。在这个不断变化的病毒动态中,预测性模型方法可以作为一个整体工具。这一流行病进一步触发了前所未有的社交媒体的使用。本文件旨在提出一种方法,利用社交媒体,特别是Twitter,预测即将发生的与COVID-19案件有关的情况。在本研究报告中,我们设法了解COVID-19相关推文的激增如何表明案件有所增加。这一预期分析可以用来帮助管理员及时分配资源,以减轻损害的严重性。我们用词嵌入来捕捉到推文的语义含义,我们找出重要的方面。我们的方法预测,在15天零30天的周期里,R2分数分别为0.80和0.62。最后,我们解释了SD的专题用途。