This paper proposes a methodology for exploring how linguistic behaviour on social media can be used to explore societal reactions to important events such as those that transpired during the SARS CoV2 pandemic. In particular, where spatial and temporal aspects of events are important features. Our methodology consists of grounding spatial-temporal categories in tweet usage trends using time-series analysis and clustering. Salient terms in each category were then identified through qualitative comparative analysis based on scaled f-scores aggregated into hand-coded categories. To exemplify this approach, we conducted a case study on the first wave of the coronavirus in Italy. We used our proposed methodology to explore existing psychological observations which claimed that physical distance from events affects what is communicated about them. We confirmed these findings by showing that the epicentre of the disease and peripheral regions correspond to clear time-series clusters and that those living in the epicentre of the SARS CoV2 outbreak were more focused on solidarity and policy than those from more peripheral regions. Furthermore, we also found that temporal categories corresponded closely to policy changes during the handling of the pandemic.

翻译：本文提出了一种方法论，探讨如何利用社交媒体上的语言行为来揭示社会对重大事件（如SARS CoV2疫情期间发生的事件）的反应，特别是当事件的时空特征至关重要时。我们的方法论包括通过时间序列分析和聚类，将时空类别建立在推文使用趋势的基础上。随后，基于缩放F分数（scaled f-scores），通过定性比较分析识别每个类别中的显著术语，并将其整合到人工编码的类别中。为展示这一方法，我们以意大利第一波新冠疫情为例进行了案例研究。我们运用所提出的方法论，检验了已有的心理学观察结论，该结论声称与事件的地理距离会影响人们对事件的表述方式。通过展示疫情震中与边缘区域对应明显的时间序列聚类，我们证实了这些发现：与边缘区域的居民相比，生活在SARS CoV2疫情震中的人更关注团结与政策。此外，我们还发现，时间类别与疫情期间政策变化高度吻合。