Social media data extraction for disaster management aid using deep learning techniques

The process of minimizing the damage being caused by disasters through information gathering, information sharing, disaster planning, problem-solving, and decision-making is known as disaster management. Social media generates a lot of information that can be used to provide disaster relief organizations with crucial information. The method under discussion is data extraction from social media during disasters, as social media generates a lot of complex data, much of which is not effectively utilized. Therefore, this study offers a technique for obtaining information from Twitter, the primary social media source, to locate and distribute the crucial aid required in emergency and catastrophe situations.Two distinct classification problems are covered in the study. Firstly, preprocessing the classification of images that are relevant to our inquiry is the first task and secondly, classifying four different types of natural disasters, including earthquakes, floods, cyclones, and wildfires. In this study, we employ Convolution Neural Network (CNN) designs along with the help of Transfer Learning (TL) architectures, wherein the model is trained and tested using the secondary data set. Furthermore, real-time tweets and images that are being extracted from Twitter are validated on the trained models and the accuracy is noted. Additional data pretreatment methods like image augmentation have also been used for preprocessing. Transfer Learning, bottleneck feature of inceptionV3 and a fine-tuning model have been included, following the disaster classification through the CNN model as a means to improve our accuracy up to 98.14%, and attaining 0.82 Precision, 0.86 Recall, and 0.84 F1-Score for Cyclone; 0.96 Precision, 0.89 Recall, and 0.92 F1Score for Earthquake; 0.74 Precision, 0.95 Recall, and 0.83 F1-Score for Flood and 0.97 Precision, 0.96 Recall, and 0.96 F1-Score for Wildfire classification. Disaster can manifest itself in different forms and generally have a negative impact on the biosphere, resulting in loss of property and the environment. This study aims to effectively exercise the power of social media data for disaster management aid.