In the absence of nationwide mass testing for an emerging health crisis, alternative approaches could provide necessary information efficiently to aid policy makers and health bodies when dealing with a pandemic. The following work presents a methodology by which Twitter data surrounding the first wave of the COVID-19 pandemic in the UK is harvested and analysed using two main approaches. The first is an investigation into localized outbreak predictions by developing a prototype early-warning system using the distribution of total tweet volume. The temporal lag between the rises in the number of COVID-19 related tweets and officially reported deaths by Public Health England (PHE) is observed to be 6–27 days for various UK cities which matches the temporal lag values found in the literature. To better understand the topics of discussion and attitudes of people surrounding the pandemic, the second approach is an in-depth behavioural analysis assessing the public opinion and response to government policies such as the introduction of face-coverings. Using topic modelling, nine distinct topics are identified within the corpus of COVID-19 tweets, of which the themes ranged from retail to government bodies. Sentiment analysis on a subset of mask related tweets revealed sentiment spikes corresponding to major news and announcements. A Named Entity Recognition (NER) algorithm is trained and applied in a semi-supervised manner to recognise tweets containing location keywords within the unlabelled corpus and achieved a precision of 81.6%. Overall, these approaches allowed extraction of temporal trends relating to PHE case numbers, popular locations in relation to the use of face-coverings, and attitudes towards face-coverings, vaccines and the national ‘Test and Trace’ scheme.

在没有针对新出现的健康危机进行全国性大规模检测的情况下，替代方法可以有效地提供必要的信息，以在应对大流行时帮助决策者和卫生机构。以下工作介绍了一种方法，该方法使用两种主要方法收集和分析有关英国第一波 COVID-19 大流行的 Twitter 数据。第一个是通过使用推文总量分布开发原型预警系统来研究局部爆发预测。据观察，英国各个城市的 COVID-19 相关推文数量增加与英格兰公共卫生部 (PHE) 官方报告的死亡人数之间的时间滞后为 6-27 天，这与文献中发现的时间滞后值相匹配。为了更好地了解人们围绕大流行的讨论主题和态度，第二种方法是深入的行为分析，评估公众舆论和对政府政策的反应，例如引入蒙面。使用主题建模，在 COVID-19 推文的语料库中确定了九个不同的主题，其中的主题范围从零售到政府机构。对面具相关推文子集的情绪分析揭示了与主要新闻和公告相对应的情绪峰值。命名实体识别 (NER) 算法以半监督方式进行训练和应用，以识别未标记语料库中包含位置关键字的推文，并达到 81.6% 的精度。总体而言，这些方法允许提取与 PHE 病例数相关的时间趋势，