论文标题

检查Covid-19引起的社区情感动态大流行:澳大利亚一个州的案例研究

Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from A State in Australia

论文作者

Zhou, Jianlong, Yang, Shuiqiao, Xiao, Chun, Chen, Fang

论文摘要

2019年新型冠状病毒病(Covid-19)的爆发对世界各地的人们的日常生活造成了前所未有的影响。政府实施了各种措施和政策,例如锁定和社会持续性,以在大流行时期对抗该疾病。这些措施和政策以及病毒本身可能会给抑郁症,焦虑,悲伤等人带来不同的心理健康问题。在本文中,我们利用Twitter用户发布的大量文本数据来分析居住在大流行时期的澳大利亚新南威尔士州(NSW)(NSW)的人们的情绪动态。与主要关注国家级别和静态情感分析的现有工作不同,我们分析了细粒度的地方政府地区(LGA)的情感动态。基于对大约9400万条推文的分析,该推文在五个月内位于新南威尔士州不同LGA的大约1.8.3亿个用户发布,我们发现新南威尔士州的人们在大流行期间表现出了整体积极的情感极性,而COVID-19的大流行则降低了整体积极的情感极性。对LGA中情感的细粒度分析发现,尽管在研究期间大部分时间大部分时间都有主导的积极情绪,但一些LGA的情感变化从正面变为负。这项研究还分析了Twitter中热门话题所传达的感性动态,例如政府政策(例如澳大利亚的求职者计划,锁定,社交活动)以及重点的社交活动(例如Ruby Princess Cruise)。结果表明,政策和事件确实影响了人们的整体情绪,他们在不同阶段对人们的整体情感影响不同。

The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has caused unprecedented impacts to people's daily life around the world. Various measures and policies such as lockdown and social-distancing are implemented by governments to combat the disease during the pandemic period. These measures and policies as well as virus itself may cause different mental health issues to people such as depression, anxiety, sadness, etc. In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period. Different from the existing work that mostly focuses the country-level and static sentiment analysis, we analyse the sentiment dynamics at the fine-grained local government areas (LGAs). Based on the analysis of around 94 million tweets that posted by around 183 thousand users located at different LGAs in NSW in five months, we found that people in NSW showed an overall positive sentimental polarity and the COVID-19 pandemic decreased the overall positive sentimental polarity during the pandemic period. The fine-grained analysis of sentiment in LGAs found that despite the dominant positive sentiment most of days during the study period, some LGAs experienced significant sentiment changes from positive to negative. This study also analysed the sentimental dynamics delivered by the hot topics in Twitter such as government policies (e.g. the Australia's JobKeeper program, lockdown, social-distancing) as well as the focused social events (e.g. the Ruby Princess Cruise). The results showed that the policies and events did affect people's overall sentiment, and they affected people's overall sentiment differently at different stages.

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