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Multimedia content in social media platforms provides significant informationduring disaster events. The types of information shared include reports ofinjured or deceased people, infrastructure damage, and missing or found people,among others. Although many studies have shown the usefulness of both text andimage content for disaster response purposes, the research has been mostlyfocused on analyzing only the text modality in the past. In this paper, wepropose to use both text and image modalities of social media data to learn ajoint representation using state-of-the-art deep learning techniques.Specifically, we utilize convolutional neural networks to define a multimodaldeep learning architecture with a modality-agnostic shared representation.Extensive experiments on real-world disaster datasets show that the proposedmultimodal architecture yields better performance than models trained using asingle modality (e.g., either text or image).

Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response