  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.
More specifically, we employed several transformers both individually and in
combination, so as to differentiate between relevant and non-relevant Twitter
posts, achieving the highest F1-score of 0.87.
