论文标题
分析Covid-19的社会影响：大流行初期的一项研究
Analyzing Societal Impact of COVID-19: A Study During the Early Days of the Pandemic
论文作者
论文摘要
在本文中，我们收集和研究Twitter通信，以了解大流行初期，美国19日在美国对美国的社会影响。随着感染迅速飙升，用户上了Twitter，要求人们自我隔离和隔离自己。用户还要求关闭学校，酒吧和餐馆以及城市和州的封锁。我们通过识别和跟踪趋势相互关联的主题标签来有条不紊地收集推文。我们首先将主题标签手动分为六个主要类别，即1）库维德将军，2）隔离，3）恐慌购买，4）封闭，5）封锁和6）挫折和希望，并研究这些主题标签中推文的时间演变。我们对所有主题标签群体共有的单词进行语言分析，并针对每个主题标签群体进行特定，并确定人们的主要关注点为大流行（例如，探索bidets作为厕纸的替代品）。我们进行情感分析，我们的调查表明，人们对学校的关闭做出了积极反应，并且对由于恐慌购买而缺乏必需品的可用性负面反应。我们采用最先进的语义角色标签方法来识别动作词，然后利用基于LSTM的依赖解析模型来分析动作词的背景（例如，动词交易伴随着诸如焦虑，压力和危机之类的名词）。最后，我们开发了一种可扩展的种子主题建模方法，以自动将推文分类和隔离到主题标签组中，并在实验上验证我们的主题模型提供了类似于手动分组的分组。我们的研究提出了一种系统的方式来构建人们对大流行的反应的汇总情况，并为未来细粒度的语言和行为分析奠定了基础。
In this paper, we collect and study Twitter communications to understand the societal impact of COVID-19 in the United States during the early days of the pandemic. With infections soaring rapidly, users took to Twitter asking people to self isolate and quarantine themselves. Users also demanded closure of schools, bars, and restaurants as well as lockdown of cities and states. We methodically collect tweets by identifying and tracking trending COVID-related hashtags. We first manually group the hashtags into six main categories, namely, 1) General COVID, 2) Quarantine, 3) Panic Buying, 4) School Closures, 5) Lockdowns, and 6) Frustration and Hope}, and study the temporal evolution of tweets in these hashtags. We conduct a linguistic analysis of words common to all hashtag groups and specific to each hashtag group and identify the chief concerns of people as the pandemic gripped the nation (e.g., exploring bidets as an alternative to toilet paper). We conduct sentiment analysis and our investigation reveals that people reacted positively to school closures and negatively to the lack of availability of essential goods due to panic buying. We adopt a state-of-the-art semantic role labeling approach to identify the action words and then leverage a LSTM-based dependency parsing model to analyze the context of action words (e.g., verb deal is accompanied by nouns such as anxiety, stress, and crisis). Finally, we develop a scalable seeded topic modeling approach to automatically categorize and isolate tweets into hashtag groups and experimentally validate that our topic model provides a grouping similar to our manual grouping. Our study presents a systematic way to construct an aggregated picture of peoples' response to the pandemic and lays the groundwork for future fine-grained linguistic and behavioral analysis.
