Active learning for event detection in support of disaster analysis applications

Disaster analysis in social media content is one of the interesting research domains having an abundance of data. However, there is a lack of labeled data that can be used to train machine learning models for disaster analysis applications. Active learning is one of the possible solutions to such a problem. To this aim, in this paper, we propose and assess the efficacy of an active learning-based framework for disaster analysis using images shared on social media outlets. Specifically, we analyze the performance of different active learning techniques under several sampling and disagreement strategies. Moreover, we collect a large-scale dataset covering images from eight common types of natural disasters. The experimental results show that the use of active learning techniques for disaster analysis in social media images results in a performance comparable to that obtained using human-annotated images with fewer data samples, and could be used in frameworks for disaster analysis in images without the tedious job of manual annotation.