Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis.
Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis.
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DOI：
10.2196/40323
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发表时间：
2022-10-05
影响因子：
7.4
通讯作者：
Ananiadou, Sophia
中科院分区：
文献类型：
--
作者：
Alhuzali, Hassan;Zhang, Tianlin;Ananiadou, Sophia
关键词：
近年来，COVID-19疫情给公共卫生、社会和经济带来了巨大变化。社交媒体为人们在疫情期间讨论健康问题、生活状况和政策提供了一个平台，使政策制定者能够利用这些内容分析公众的情绪和态度，以进行决策。这项研究的目的是使用基于深度学习的方法，通过Twitter上的比较地理定位和文本挖掘分析，了解英国公众对COVID-19疫情相关话题的情绪。我们提取了来自英国48个不同城市的超过500，000条与COVID-19相关的推文，数据涵盖过去两年（2020年2月至2021年11月）。我们利用三种先进的基于深度学习的主题建模模型来分析英国推文的情感、情绪和主题：SenticNet 6用于情感分析，SpanEmo用于情感识别，以及组合主题建模（CTM）。我们观察到，随着流行病学情况和疫苗接种情况在2年内发生变化，推文数量发生了显著变化。于二零二零年一月至二零二零年二月，由于英国爆发COVID-19，推文数量大幅增加。然后，截至2020年2月，推文数量逐渐下降。此外，随着2021年11月在英国发现COVID-19 Omicron变种，推文数量再次增长。我们的研究结果揭示了人们对COVID-19相关话题的态度和情绪。在情绪方面，大约60%的推文是积极的，20%是中立的，20%是负面的。在情绪方面，人们在2020年初倾向于表达高度积极的情绪，而在2021年底随着时间的推移表达高度消极的情绪。在疫情期间，话题也发生了变化。通过对Twitter的大规模文本挖掘，我们的研究发现，英国不同城市的公众情绪和关于COVID-19大流行的话题存在显著差异。此外，有效的基于位置和基于时间的比较分析可以用来跟踪人们的思想和感受，并了解他们的行为。根据我们的分析，积极的态度在大流行期间很常见;乐观和期待是主要情绪。随着疫情的爆发和流行病学的变化，政府制定了控制措施和疫苗接种政策，话题也随着时间的推移而转移。总的来说，表情符号、情绪、情感和主题的比例和表达方式在地理和时间上各不相同。因此，我们从Twitter上探索公众对大流行病的情绪和话题的方法可能有助于了解公共政策在特定地理区域的接受情况。
In recent years, the COVID-19 pandemic has brought great changes to public health, society, and the economy. Social media provide a platform for people to discuss health concerns, living conditions, and policies during the epidemic, allowing policymakers to use this content to analyze the public emotions and attitudes for decision-making. The aim of this study was to use deep learning–based methods to understand public emotions on topics related to the COVID-19 pandemic in the United Kingdom through a comparative geolocation and text mining analysis on Twitter. Over 500,000 tweets related to COVID-19 from 48 different cities in the United Kingdom were extracted, with the data covering the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning–based models for topic modeling to geospatially analyze the sentiment, emotion, and topics of tweets in the United Kingdom: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and combined topic modeling (CTM). We observed a significant change in the number of tweets as the epidemiological situation and vaccination situation shifted over the 2 years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the United Kingdom. Then, the number of tweets gradually declined as of February 2020. Moreover, with identification of the COVID-19 Omicron variant in the United Kingdom in November 2021, the number of tweets grew again. Our findings reveal people’s attitudes and emotions toward topics related to COVID-19. For sentiment, approximately 60% of tweets were positive, 20% were neutral, and 20% were negative. For emotion, people tended to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions over time toward the end of 2021. The topics also changed during the pandemic. Through large-scale text mining of Twitter, our study found meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people’s thoughts and feelings, and to understand their behaviors. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak and epidemiological change, the government developed control measures and vaccination policies, and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions, and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area.
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