An Empirical Study on Characterizing Natural Disasters in Class Imbalanced Social Media Data using Weak Supervision.

Supervised learning has proven to be successful in classifying both class balanced and imbalanced data when a strong supervision signal is available. However, generating the supervision signal (eg: ground truth labels) is expensive and a major bottleneck of supervised learning. To curtail this, we rely on the theory of noisy learning and weak supervision to generate supervision signals. In this work, we utilize a noisy labeled dataset to train several class balanced and imbalanced machine learning models and compare the results to observe how efficient the models trained on silver standard dataset are in identifying ground truth labels. We demonstrate the approach on a natural disasters application which contains data from three different natural disasters. Our results demonstrate that theory of noisy learning can be utilized to build models via weak supervision for both class balanced and imbalanced data from social media sources for natural disasters application.