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Title: Multimodal Semi-supervised Learning for Disaster Tweet Classification
During natural disasters, people often use social media platforms, such as Twitter, to post information about casualties and damage produced by disasters. This information can help relief authorities gain situational awareness in nearly real time, and enable them to quickly distribute resources where most needed. However, annotating data for this purpose can be burdensome, subjective and expensive. In this paper, we investigate how to leverage the copious amounts of unlabeled data generated on social media by disaster eyewitnesses and affected individuals during disaster events. To this end, we propose a semi-supervised learning approach to improve the performance of neural models on several multimodal disaster tweet classification tasks. Our approach shows significant improvements, obtaining up to 7.7% improvements in F-1 in low-data regimes and 1.9% when using the entire training data. We make our code and data publicly available at https://github.com/iustinsirbu13/multimodal-ssl-for-disaster-tweet-classification.  more » « less
Award ID(s):
1741345
NSF-PAR ID:
10388181
Author(s) / Creator(s):
Date Published:
Journal Name:
The 29th International Conference on Computational Linguistics (COLING 2022)
Page Range / eLocation ID:
2711–2723
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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