Relevance Classification of Flood-related Twitter Posts via Multiple Transformers

Understand

In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations.

• Several interesting works and case studies of disaster analytics exploring different aspects of natural disasters have been already conducted.
• Along with the great potential, disaster analytics comes with several challenges mainly due to the nature of social media content.
• In this paper, we explore one such challenge and propose a text classification framework to deal with Twitter noisy data.