Natural and man‐made disasters potentially cause significant damage and disruption to communities. As population density increases and natural disasters become more extreme, it becomes increasingly important to both manage communications and extract information from communications to be able to mitigate the negative effects of such disasters. The emergence of social media platforms has led to new avenues for the collection and dissemination of information that is either local or global. Although social studies have revealed that social media feeds can improve effective disaster preparedness and recovery, it has been observed that the use of social media can have severe negative consequences through the rapid spread of false information leading to the inappropriate allocation of resources and in extreme cases panic and lawlessness. Rumours and false information are likely to affect appropriate corporate responses as well as, where appropriate, responses of public organizations tasked with appropriately responding to natural and man‐made disasters. The ability to identify instances of false information through the course of natural and man‐made disasters is a critical capability for corporate and public bodies in order to improve disaster management and response.

To reduce the impact of these rumours, a technique is proposed that makes use of supervised learning to differentiate between information about an actual event from information about a false one and communicating it effectively to appropriate organizations. For this purpose, 934 social media feeds were analysed using a Naïve Bayes classifier. Clearly, applied early on this technique potentially can improve the quality of disaster response and recovery and mitigate the negative consequences.

Broadly speaking, we consider this research to relate to the management of knowledge and information flow in disaster situations. Clearly, the techniques introduced in this paper, in providing individuals and organizations with access to knowledge rather than false information and rumours, will help organizations manage resources and activities during disasters more efficiently and effectively. The study concludes with the implications, limitations, and future directions.