Generalization of convolutional network to domain adaptation network for classification of disaster images on twitter

Social media evolution in the recent past has drastically changed the way the affected community responds to any disaster. Within moments of happening of any disaster the activity on social media platforms swiftly jumps many folds. The affected population starts posting messages to update their family and friends about the situation and to seek help. The high volume and variety of this social media content pose challenges for the Government authorities and rescue workers to timely respond to the needs of the people. Effective computational analysis of this information can serve as an efficient tool to boost the desired response and rescue operations during such events. Recently with the significant progress in artificial intelligence systems, many machine learning methods for automatic filtration of relevant messages have been proposed by researchers. However, most of these methods follow supervised learning techniques that need abundant labeled data for training which is hard to arrange at the onset of the disaster. To overcome this constraint for a disaster in progress, our study proposes a novel deep learning convolutional network based on the concept of Unsupervised Domain Adaptation (UDA) that can classify unlabeled data for a new disaster (target domain) using the labeled data available from previous disaster (source domain). Through experiments performed on the images of seven disasters, the current work demonstrates that the proposed UDA method using the Maximum Mean Discrepancy (MMD) metric outperforms different state-of-the-art methods even without having labeled data for the new disaster.