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