Twitter-based disaster management system using data mining

Social media is an essential part of life for most people around. No wonder even during emergencies like flood or cyclone, more and more people look up to Twitter, Facebook, WhatsApp groups, etc., for immediate assistance. This helps to get data from even remote places and from small groups which will be difficult to reach. This sheer amount of data generated during a short span of time is also the challenge in this approach. Even when there are resources available for help, many requests could go unnoticed. This paper addresses above-mentioned problem by collecting the generated requests for help and resource availability and plot the location in the map. Request data shall be analysed using three machine learning algorithms called linear ridge regression, SGD classifier and Naive Bayes algorithm for the initial filtering and will be passed through natural language processing to match needs and offers within a given geographic boundary. The system is working with 96% accuracy for linear ridge regression and Naive Bayes classifier and 95% accuracy for SGD classifier. The report shall be published to provide a centralized status of requests. This brings more efficient management of disaster situations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.