[论文解读] Examination of community sentiment dynamics due to covid-19 pandemic: a case study from Australia

Jianlong Zhou, Shuiqiao Yang+2 more|arXiv (Cornell University)|Jul 7, 2020

Misinformation and Its Impacts被引用 23

一句话总结

本研究利用在新冠疫情期间从澳大利亚新南威尔士州18.3万个用户收集的9400万条地理标记至地方政府区域（LGA）的推文，分析了新南威尔士州的细粒度情感动态。研究发现，尽管整体情感呈积极趋势，但在疫情期间整体积极情感有所下降，部分LGA从积极转为消极情感，这主要由政府政策和高影响力事件（如“鲁比公主号”邮轮疫情爆发）所驱动。

ABSTRACT

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 lock-down 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, lock-down, 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.

研究动机与目标

• 考察新冠疫情如何影响澳大利亚新南威尔士州地方政府区域（LGA）层面的情感动态。
• 识别不同LGA之间情感极性的差异，超越国家层面或静态情感分析的局限。
• 调查特定政府政策（如JobKeeper计划和封锁措施）对公众情感的影响。
• 评估高影响力社会事件（如“鲁比公主号”邮轮事件）对社区情感的影响。
• 理解在细致地理尺度下，情感如何随政策和事件触发而随时间演变。

提出的方法

• 使用了约9400万条从澳大利亚新南威尔士州地方政府区域（LGA）地理标记用户收集的推文数据集。
• 应用自然语言处理（NLP）技术对文本数据进行情感分析，将情感极性分类为积极、消极或中性。
• 在LGA层面进行细粒度情感分析，以捕捉不同地区之间的本地化情感差异。
• 追踪五个月内的情感趋势，以识别疫情期间公众情感的时序变化。
• 通过基于事件的时间分析，将情感变化与特定事件和政策公告（如封锁和JobKeeper计划）相关联。
• 识别并分析与公共卫生政策和社会事件相关的Twitter热门话题，以评估其对情感动态的影响。

实验结果

研究问题

• RQ1 在新冠疫情早期阶段，澳大利亚新南威尔士州地方政府区域（LGA）层面的情感极性如何随时间变化？
• RQ2 政府政策（如JobKeeper计划和社会距离措施）在多大程度上影响了不同LGA的公众情感？
• RQ3 重大社会事件（如“鲁比公主号”邮轮事件）如何影响当地社区的情感动态？
• RQ4 是否存在某些LGA经历了从显著积极到消极情感的显著转变？其背后的原因是什么？
• RQ5 与国家层面或聚合区域情感分析相比，以LGA层面分析情感动态有何不同？

主要发现

• 尽管整体情感趋势为积极，但疫情期间新南威尔士州整体积极情感极性出现了可测量的下降。
• 大量LGA经历了从以积极为主到消极情感的转变，表明存在局部化的心理健康影响。
• 政府政策（如JobKeeper计划和封锁措施）与可检测到的情感变化相关，但其方向和幅度因疫情阶段而异。
• 高影响力事件（如“鲁比公主号”邮轮事件）引发了情感的急剧、局部性下降，尤其在受影响的LGA中更为明显。
• 对政策和事件触发的情感反应并非一致，不同LGA在情感转变的时间模式上表现出显著差异。
• 细粒度的LGA层面分析揭示了对情感变化更高的敏感度，优于国家层面或聚合区域分析。