Near-real-time earthquake-induced fatality estimation using crowdsourced data and large-language models

Abstract. The impact of geohazards on the mental health of local populations is well recognised but understudied. We used natural language processing (NLP) of Twitter (now known as X) posts (n=10 341) to analyse the sentiments expressed in relation to a pre-eruptive seismic unrest and a subsequent volcanic eruption in Iceland in 2019–2021. We show that although these geohazards were of a small size and caused negligible material damage, they were associated with a measurable change in expressed emotions in the local population. The seismic unrest was associated with predominantly negative sentiments (positive-to-negative sentiment ratio of 1:1.3), but the eruption was associated with predominantly positive sentiments (positive-to-negative sentiment ratio of 1.4:1). We demonstrate a cost-effective tool for gauging public discourse that could be used in risk management.

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Near-real-time earthquake-induced fatality estimation using crowdsourced data and large-language models

Cited by 2 publications

References 38 publications

Keyword Extraction in Arabic and English using Page Rank Algorithm

Keyword Extraction in Arabic and English using Page Rank Algorithm

Brief communication: Small-scale geohazards cause significant and highly variable impacts on emotions

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