People around the world have experienced fundamental transformations during mass events. The Industrial Revolution, World War II, and the collapse of the Berlin Wall are some of the cases that have caused radical societal changes. COVID-19 has also been a process of mass experiences regarding society. Determining the mass impact the pandemic has had on society shows that the pandemic is facilitating the transition to the so-called new normal. Istanbul is a multi-identity city where 16 million people have intensely experienced the pandemic’s impact. While determining the identities of cities in the world, one can see that different city structures provide different data sets. This study models a machine learning algorithm suitable for the data set we’ve determined for the 39 different districts of Istanbul and 82 different features of Istanbul. The aim of the study is to indicate the changing societal trends during the COVID-19 pandemic using machine learning techniques. Thus, this work contributes to the literature and real life in terms of redesigning cities for the post-COVID19 period. Another contribution of this study is that the proposed methodology provides clues on what people in cities consider important during a pandemic.

中文翻译：

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通过机器学习模型进行特征选择，以识别 COVID-19 大流行期间公众不断变化的优先事项

世界各地的人们在大规模事件中经历了根本性的转变。工业革命、第二次世界大战和柏林墙的倒塌是导致社会彻底变革的一些例子。COVID-19 也是一个关于社会的大众体验过程。确定大流行对社会的大规模影响表明，大流行正在促进向所谓的新常态过渡。伊斯坦布尔是一个多重身份的城市，有 1600 万人强烈体验了这场大流行病的影响。在确定世界城市的身份时，可以看到不同的城市结构提供不同的数据集。这项研究模拟了一种机器学习算法，该算法适用于我们为伊斯坦布尔的 39 个不同地区和伊斯坦布尔的 82 个不同特征确定的数据集。该研究的目的是使用机器学习技术表明 COVID-19 大流行期间不断变化的社会趋势。因此，这项工作在重新设计后 COVID19 时期的城市方面有助于文献和现实生活。这项研究的另一个贡献是，所提出的方法为城市中的人们认为在大流行期间重要的事情提供了线索。