Multimodal Classification of Social Media Disaster Posts With Graph Neural Networks and Few-Shot Learning

Social media has emerged during the last decade as a potential information source in crisis scenarios, providing data in real-time or just after the occurrence of the event. Nevertheless, current social media data acquisition procedures result in datasets, presenting myriad non-informative content, which hinders posterior analyses. While previous studies have used machine learning to address this issue, they typically require many labeled examples, hardening their use in a real-world scenario. Moreover, social media posts tend to be multimodal, which adds complexity to how these data should be represented. This paper extends upon our previous work and presents a new method for identifying the most informative content related to an event in textual and visual data through few-shot learning. The results show that this method outperforms existing approaches in both performance and efficiency, offering a valuable solution for a timely analysis of crisis-related social media data and advancing research in this area.