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This paper proposes a methodology for exploring how linguistic behaviour onsocial media can be used to explore societal reactions to important events suchas those that transpired during the SARS CoV2 pandemic. In particular, wherespatial and temporal aspects of events are important features. Our methodologyconsists of grounding spatial-temporal categories in tweet usage trends usingtime-series analysis and clustering. Salient terms in each category were thenidentified through qualitative comparative analysis based on scaled f-scoresaggregated into hand-coded categories. To exemplify this approach, we conducteda case study on the first wave of the coronavirus in Italy. We used ourproposed methodology to explore existing psychological observations whichclaimed that physical distance from events affects what is communicated aboutthem. We confirmed these findings by showing that the epicentre of the diseaseand peripheral regions correspond to clear time-series clusters and that thoseliving in the epicentre of the SARS CoV2 outbreak were more focused onsolidarity and policy than those from more peripheral regions. Furthermore, wealso found that temporal categories corresponded closely to policy changesduring the handling of the pandemic.

Exploring Spatial-Temporal Variations of Public Discourse on Social Media: A Case Study on the First Wave of the Coronavirus Pandemic in Italy