Improving emergency response operations in maritime accidents using social media with big data analytics: a case study of the MV Wakashio disaster

This paper aims to explore how big data analytics (BDA) emerging technologies crossed with social media (SM). Twitter can be used to improve decision-making before and during maritime accidents. We propose a conceptual early warning system called community alert and communications system (ComACom) to prevent future accidents. Design/methodology/approach – Based on secondary data, the authors developed a narrative case study of the MV Wakashio maritime disaster. The authors adopted a post-constructionist approach through the use of media richness and synchronicity theory, highlighting wider community voices drawn from social media (SM), particularly Twitter. The authors applied BDA techniques to a dataset of real-time tweets to evaluate the unfolding operational response to the maritime emergency. Findings – The authors reconstituted a narrative of four escalating sub-events and illustrated how critical decisions taken in an organisational and institutional vacuum led to catastrophic consequences. We highlighted the specific roles of three main stakeholders (the ship's organisation, official institutions and the wider community). Our study shows that SM enhanced with BDA, embedded within our ComACom model, can better achieve collective sense-making of emergency accidents. Research limitations/implications – This study is limited to Twitter data and one case. Our conceptual model needs to be operationalised. Practical implications – ComACom will improve decision-making to minimise human errors in maritime accidents. Social implications – Emergency response will be improved by including the voices of the wider community. Originality/value – ComACom conceptualises an early warning system using emerging BDA/AI technologies to improve safety in maritime transportation.