2022/03/16 by Víctor Ponce-López, Ponce-López, Victor, Catalina Spataru +1
Earth and Planetary Sciences · Social Sciences · #Disaster Management and Resilience #FOS: Computer and information sciences #Public Relations and Crisis Communication #Social and Information Networks (cs.SI) #Tropical and Extratropical Cyclones Research
paper · pdf · doi:10.48550/arxiv.2203.08753
openalex publication_date 2022/03/16 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
This paper analyses social media data in multiple disaster-related collections of floods and heat waves in the UK. The proposed method uses machine learning classifiers based on deep bidirectional neural networks trained on benchmark datasets of disaster responses and extreme events. The resulting models are applied to perform sentiment and qualitative analysis of inferred topics in text data. We further analyse a set of behavioural indicators and match them with climate variables via decoding synoptical records to analyse thermal comfort. We highlight the advantages of aligning behavioural indicators along with climate variables to provide with additional valuable information to be considered especially in different phases of a disaster and applicable to extreme weather periods. The positiveness of messages is around 8% for disaster, 1% for disaster and medical response, 7% for disaster and humanitarian related messages. This shows the reliability of such data for our case studies. We show the transferability of this approach to be applied to any social media data collection.