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In recent years, social media has been widely explored as a potential sourceof communication and information in disasters and emergency situations. Severalinteresting works and case studies of disaster analytics exploring differentaspects of natural disasters have been already conducted. Along with the greatpotential, disaster analytics comes with several challenges mainly due to thenature of social media content. In this paper, we explore one such challengeand propose a text classification framework to deal with Twitter noisy data.More specifically, we employed several transformers both individually and incombination, so as to differentiate between relevant and non-relevant Twitterposts, achieving the highest F1-score of 0.87.

Relevance Classification of Flood-related Twitter Posts via Multiple Transformers