Randomized Convolutional Neural Network Architecture for Eyewitness Tweet Identification During Disaster

During a disaster, Twitter is flooded with disaster-related information. Among huge disaster-related Twitter posts, a fraction of them is posted by the eyewitness of disaster. The post of an eyewitness of the disaster contains an authentic description of the disaster. Therefore, eyewitness disaster-related posts are preferred over all other sources of information to know the floor reality of the disaster. In this work, we have used a convolutional neural network (CNN) with randomly initialized weights to extract features from the textual contents of the tweets and proposed three different random neural network-based models. The feature extracted from the untrained random convolutional neural network (RCNN) is passed through a trainable dense neural network (DNN), echo state network (ESN), and extreme learning machine (ELM) to identify eyewitness tweets. The proposed system is validated with hurricane, earthquake, flood, and wildfire datasets. In the extensive experiments with three different random neural network-based models such as RCNN-DNN, RCNN-ESN, RCNN-ELM, and other machine learning and deep learning models such as KNN, Naive Bayes, Decision Tree, Convolutional neural network, and Dense Neural Network, the RCNN-DNN model outperformed all the other models. The RCNN-DNN model achieved impressive performance with a weighted F1-scores of 0.79, 0.86, 0.79, and 0.85 for hurricane, earthquake, flood, and wildfire, respectively.