Content-based user classifier to uncover information exchange in disaster-motivated networks.

ABSTRACT: Disasters strike communities around the world, with a reduced time-frame for warning and action leaving behind high rates of damage, mortality, and years in rebuilding efforts. For the past decade, social media has indicated a positive role in communicating before, during, and after disasters. One important question that remained un-investigated is that whether social media efficiently connect affected individuals to disaster relief agencies, and if not, how AI models can use historical data from previous disasters to facilitate information exchange between the two groups. In this study, the BERT model is first fine-tuned using historical data and then it is used to classify the tweets associated with hurricanes Dorian and Harvey based on the type of information provided; and alongside, th

SUBMITTER: Babvey P 

PROVIDER: S-EPMC8594803 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature