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Article type: Research Article
Authors: Singh, Pardeepa; * | Singh, Monikaa | Singh, Nitin Kumara | Das, Prativab | Chand, Satisha
Affiliations: [a] School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, India | [b] Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India
Correspondence: [*] Corresponding author. Pardeep Singh, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, India. E-mail: [email protected].
Abstract: Social media platforms play vital roles in disseminating information during crisis situations. Many rescue agencies, media outlets, and volunteers regularly monitor this data to identify and analyze disasters, ultimately mitigating life risks. However, effectively categorizing these messages based on information types is crucial for enhancing the situational awareness of emergency responders. This paper addresses the challenge of analyzing informal crisis-related social media texts by classifying disaster event tweets into 10 humanitarian categories associated with 19 major natural disaster events. We fine-tune seven state-of-the-art pre-trained transformer models and compare their performance with the recently introduced domain-specific models, i.e., CrisisTransformers. We empirically found that CrisisTransformers outperform seven strong baseline transformer models in classifying disaster-specific tweets from the HumAID dataset, achieving a macro-averaged F1 score of 0.77. Our work contributes to the crisis computing field by improving the classification of disaster-related tweets and enhancing the capabilities of emergency responders and disaster management organizations.
Keywords: Transformers, crisis computing, disaster classification, Twitter, disaster response
DOI: 10.3233/JIFS-219419
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
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