Relevance Classification of Flood-related Twitter Posts via Multiple Transformers

TL;DR

This paper presents a transformer-based framework for classifying flood-related Twitter posts as relevant or non-relevant, addressing the challenge of noisy social media data in disaster analytics.

Contribution

It introduces a novel approach combining multiple transformers for relevance classification of disaster-related social media content.

Findings

Achieved highest F1-score of 0.87 in relevance classification

Demonstrated effectiveness of combining multiple transformers

Addresses social media noise in disaster communication

Abstract

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.