Workhybrid2023

Natural Disaster Tweets Classification Using Multimodal Data

Bashir Alam · Zubaida Fatima · Salman Shaikh

18 citations0/0 files storedConference on Empirical Methods in Natural Language Processing (EMNLP)

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Public Relations and Crisis Communication Disaster Management and Resilience Text and Document Classification Technologies

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.

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References

2018CrisisMMD: Multimodal Twitter Datasets from Natural Disasters 2018Damage Identification in Social Media Posts using Multimodal Deep Learning.2021Disaster Image Classification by Fusing Multimodal Social Media Data 2018A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria 2020Multimodal Multi-Task Financial Risk Forecasting 2019Multimodal Analysis of Disaster Tweets 2019Detecting Informative Tweets during Disaster using Deep Neural Networks 2020Crisis-DIAS: Towards Multimodal Damage Analysis - Deployment, Challenges and Assessment

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