Disaster Tweets Classification for Multilingual Tweets Using Machine Learning Techniques

Abstract

Natural disasters have dire consequences for communities, leading to loss of life, property destruction and environmental devastation. Effective disaster response necessitates prompt and coordinated actions. Social media platforms, particularly Twitter, have emerged as invaluable assets in disaster management. With an enormous daily influx of tweets, Twitter data presents an opportunity to gain valuable insights for tracking and responding to disasters. However, sifting through the vast volume of regular content to identify relevant tweets poses a significant challenge. Furthermore, the global nature of Twitter introduces an added layer of complexity with tweets in different languages. Recent advancements in deep learning techniques provide promising solutions for addressing this challenge, enabling the identification of disaster-related information from multilingual tweets. This research proposes a comprehensive approach that leverages machine learning and deep learning models to accurately classify disaster-related tweets in multiple languages, including English, Hindi, and Bengali. The study evaluates the performance of seven Machine Learning classifiers, including Naive Bayes, Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, Decision Tree and few Deep Learning models such as LSTM, BiLSTM, BiLSTM with CNN, BERT and DistilBERT. After conducting a thorough evaluation of multiple models, it is evident that BERT and DistilBERT stand out as the top performers, consistently exhibiting exceptional accuracy and delivering consistent results across diverse language contexts.