Disaster Events Identification from Social Media Data Using Deep Machine Learning Technique

Social media has seen a boom in recent years, with its users growing worldwide. The ubiquitous nature of social media has made it a useful tool to extract useful information regarding disasters. As in case of any disaster, people upload data related to the disaster on social media which can be very useful in the timely detection of disasters and preventing loss of human lives and infrastructures. This research aims to identify disaster-related tweets from bulk data, classify them to the type of event they belong to i.e. to perform multi-classification, and identify type of humanitarian aid-related information posted in a tweet during disasters, with improved performance in terms of accuracy, F1, etc. To perform this multiclass classification i.e. to detect disastrous events and further detect the type of humanitarian information present in the tweets a DistilBert+CNN+LSTM-based framework has been proposed in this paper. The framework is based on DistilBert pre-trained embeddings and for multi-class classification, CNN+LSTM with a self-attention layer has been used. After applying the proposed framework the F1 score of 98 % was achieved in disastrous events classification and an F1 score of 88% was achieved in information classification of tweets. These results obtained from the proposed framework have shown improvement over other deep learning models that were used as part of a comparative study