论文标题
面对面：在19日大流行期间，公众对个人面膜使用情况的两极分化意见
Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic
论文作者
论文摘要
尽管越来越多的科学证据表明了个人面罩使用以降低传输速率的有效性，但单个面罩的使用已成为美国高度两极化的话题。据推测，各种政府机构的一系列政策转变促成了面具的两极分化。研究这些政策转变影响的典型方法是使用调查。但是，基于调查的方法有多个局限性：偏见的响应，样本量有限，精心制作的问题可能会偏向回答并抑制洞察力，并且随着对动态主题的响应的意见变化，回答可能很快就无关紧要。我们提出了一种新颖的方法1）1）使用多模式的人口统计学推理框架与主题建模和2）确定是否使用twitter数据的离线变化点分析来确定面具对面罩的两极分化是否有助于面具对面罩的两极分化，从而准确评估了Covid-19期间美国对美国面罩的公众情绪。首先，我们推断出单个Twitter用户的几个关键人口统计，例如他们的年龄，性别，以及他们是否是使用多模式人口统计学预测框架的大学生，并分析每个人群的平均情感。接下来，我们使用潜在Dirichlet分配（LDA）进行主题分析。最后，我们使用修剪的精确线性时间（PELT）搜索算法对情感时间序列数据进行离线变更点发现。大量Twitter数据的实验结果揭示了有关人口统计学情感对与现有调查一致的面具的多种见解。此外，我们发现两个关键的政策班次事件导致共和党人和民主党人的情感上的统计学意义变化。
In spite of a growing body of scientific evidence on the effectiveness of individual face mask usage for reducing transmission rates, individual face mask usage has become a highly polarized topic within the United States. A series of policy shifts by various governmental bodies have been speculated to have contributed to the polarization of face masks. A typical method to investigate the effects of these policy shifts is to use surveys. However, survey-based approaches have multiple limitations: biased responses, limited sample size, badly crafted questions may skew responses and inhibit insight, and responses may prove quickly irrelevant as opinions change in response to a dynamic topic. We propose a novel approach to 1) accurately gauge public sentiment towards face masks in the United States during COVID-19 using a multi-modal demographic inference framework with topic modeling and 2) determine whether face mask policy shifts contributed to polarization towards face masks using offline change point analysis on Twitter data. First, we infer several key demographics of individual Twitter users such as their age, gender, and whether they are a college student using a multi-modal demographic prediction framework and analyze the average sentiment for each respective demographic. Next, we conduct topic analysis using latent Dirichlet allocation (LDA). Finally, we conduct offline change point discovery on our sentiment time series data using the Pruned Exact Linear Time (PELT) search algorithm. Experimental results on a large corpus of Twitter data reveal multiple insights regarding demographic sentiment towards face masks that agree with existing surveys. Furthermore, we find two key policy-shift events contributed to statistically significant changes in sentiment for both Republicans and Democrats.
