Extensive research has been conducted to identify, analyze and measure popular topics and public sentiment on Online Social Networks (OSNs) through text, especially during crisis events. However, little work has been done to understand
such events through pictures posted on these networks. Given the potential of visual content for influencing users’ thoughts and emotions, we perform a large-scale analysis to study and compare popular themes and sentiment across images and textual content posted on Facebook during the terror attacks that took place in Paris in 2015. We propose a generalizable and highly automated 3-tier pipeline which utilizes state-of-the-art computer vision techniques to extract high-level human understandable image descriptors.