Abstract

Social media is inevitably the most abundant source of actionable information in times of natural disasters. Most of the data is either available in the form of text, images or videos. Real-time analysis of such data during the events of calamities poses many challenges to machine learning algorithms that require a large amount of data to perform well. Multimodal Twitter Dataset for Natural Disasters (CrisisMMD) is one such novel dataset that provides annotated textual as well as image data to researchers to aid the development of crisis response mechanism which can leverage social media platforms to extract useful information in times of crisis. In this paper, we analyze multimodal data related to seven different natural calamities like hurricanes, floods, earthquakes, etc. and propose a novel decision diffusion technique to classify them into informative and noninformative categories. The proposed methodology outperforms the text baselines by more than 4% accuracy and image baselines by more than 3%