A Framework for Enhancing Real-time Social Media Data to Improve Disaster Management Process

Social Media datasets are playing a vital role providing information that can support decision-making in nearly all domains. This is due to the fact that social media is a quick and economical approach for collecting data. It has already been proved that in case of disaster (natural or man-made) the information extracted from Social Media sites is very critical to Disaster Management Systems for response and reconstruction. This study comprises of two parts: The first proposes a framework that provides updated and filtered real time input data for the disaster management system through social media, and the second consists of a designed web user API for a structured and defined real time data input process. The aim of this study is to propose a framework that can filter and organize data from the unstructured social media sources through recognized methods and bring this retrieved data to the same level as that acquired through structured and predefined mechanisms, such as a web API. Both components are designed such that they can potentially collaborate and produce updated information for a disaster management system to carry out accurate and effective decision-making.