  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.
