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        <title>Natural Disaster Tweets Classification Using Multimodal Data</title>
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    <abstract>Social media platforms are extensively used for expressing opinions or conveying information. The information available on such platforms can be used for various humanitarian and disaster-related tasks as distributing messages in different formats through social media is quick and easy. Often this useful information during disaster events goes to waste as efficient systems don’t exist which can turn these unstructured data into meaningful format which can ultimately assist aid agencies. In disaster identification and assessment, information available is naturally multimodal, however, most existing work has been solely focused on single modalities e.g. images or texts separately. When information from different modalities are integrated , it produces significantly better results. In this paper, we have explored different models which can lead to the development of a system that deals with multimodal datasets and can perform sequential hierarchical classification. Specifically, we aim to find the damage and its severity along with classifying the data into humanitarian categories. The different stages in the hierarchical classification have had their respective models selected by researching with many different modality specific models and approaches of multimodal classification including multi task learning. The hierarchical model can give results at different abstraction levels according to the use cases. Through extensive quantitative and qualitative analysis, we show how our system is effective in classifying the multimodal tweets along with an excellent computational efficiency and assessment performance. With the help of our approach, we aim to support disaster management through identification of situations involving humanitarian tragedies and aid in assessing the severity and type of damage.</abstract>
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%0 Conference Proceedings
%T Natural Disaster Tweets Classification Using Multimodal Data
%A Basit, Mohammad
%A Alam, Bashir
%A Fatima, Zubaida
%A Shaikh, Salman
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F basit-etal-2023-natural
%X Social media platforms are extensively used for expressing opinions or conveying information. The information available on such platforms can be used for various humanitarian and disaster-related tasks as distributing messages in different formats through social media is quick and easy. Often this useful information during disaster events goes to waste as efficient systems don’t exist which can turn these unstructured data into meaningful format which can ultimately assist aid agencies. In disaster identification and assessment, information available is naturally multimodal, however, most existing work has been solely focused on single modalities e.g. images or texts separately. When information from different modalities are integrated , it produces significantly better results. In this paper, we have explored different models which can lead to the development of a system that deals with multimodal datasets and can perform sequential hierarchical classification. Specifically, we aim to find the damage and its severity along with classifying the data into humanitarian categories. The different stages in the hierarchical classification have had their respective models selected by researching with many different modality specific models and approaches of multimodal classification including multi task learning. The hierarchical model can give results at different abstraction levels according to the use cases. Through extensive quantitative and qualitative analysis, we show how our system is effective in classifying the multimodal tweets along with an excellent computational efficiency and assessment performance. With the help of our approach, we aim to support disaster management through identification of situations involving humanitarian tragedies and aid in assessing the severity and type of damage.
%R 10.18653/v1/2023.emnlp-main.471
%U https://aclanthology.org/2023.emnlp-main.471
%U https://doi.org/10.18653/v1/2023.emnlp-main.471
%P 7584-7594

Markdown (Informal)

[Natural Disaster Tweets Classification Using Multimodal Data](https://aclanthology.org/2023.emnlp-main.471) (Basit et al., EMNLP 2023)

• Natural Disaster Tweets Classification Using Multimodal Data (Basit et al., EMNLP 2023)

ACL

• Mohammad Basit, Bashir Alam, Zubaida Fatima, and Salman Shaikh. 2023. Natural Disaster Tweets Classification Using Multimodal Data. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7584–7594, Singapore. Association for Computational Linguistics.