Utilizing Machine Learning for Categorizing Disaster-Related Tweets

This study presents the development of a machine-learning model that recognizes tweets about disasters in real-time. The fast-paced development of social media, especially Twitter, has drastically changed the field of news transmission during crises. This change creates new challenges for the management of the volume, velocity, and veracity of data that are generated in such situations. The present tools do not have the capabilities of real-time processing of the huge volume of data; hence, new solutions are required. The model we are proposing addresses this problem by using advanced machine learning methods, such as natural language processing, to filter and categorize relevant information from Twitter in an efficient manner. This model seeks to increase the operational skills of disaster response teams through the provision of real-time and precise data, which is vital in enhancing the effectiveness of emergency responses. The study specifies the approach containing data collection, data preprocessing, and model optimization and points out the integration of the model with the current digital tools to facilitate the disaster management process.