Multimodal Semi-supervised Learning for Disaster Tweet Classification

Iustin Sirbu, Tiberiu Sosea, Cornelia Caragea, Doina Caragea, Traian Rebedea


Abstract
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.
Anthology ID:
2022.coling-1.239
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2711–2723
Language:
URL:
https://aclanthology.org/2022.coling-1.239
DOI:
Bibkey:
Cite (ACL):
Iustin Sirbu, Tiberiu Sosea, Cornelia Caragea, Doina Caragea, and Traian Rebedea. 2022. Multimodal Semi-supervised Learning for Disaster Tweet Classification. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2711–2723, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Multimodal Semi-supervised Learning for Disaster Tweet Classification (Sirbu et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.239.pdf
Code
 iustinsirbu13/multimodal-ssl-for-disaster-tweet-classification