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

• URL: http://arxiv.org/abs/2306.16031v1
• Date: Wed, 28 Jun 2023 08:59:50 GMT
• Title: Exploring Spatial-Temporal Variations of Public Discourse on Social Media: A Case Study on the First Wave of the Coronavirus Pandemic in Italy
• Authors: Anslow Michael and Galletti Martina
• Abstract summary: This paper proposes a methodology for exploring how linguistic behaviour on social media can be used to explore societal reactions to important events. Our methodology consists of grounding spatial-temporal categories in tweet usage trends using time-series analysis and clustering. We also found that temporal categories corresponded closely to policy changes during the handling of the pandemic.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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