Chen, Qi, Wang, Wei, Huang, Kaizhu, De, Suparna and Coenen, Frans 
ORCID: 0000-0003-1026-6649
(2020)
Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media.
In: 2020 IEEE International Conference on Smart Computing (SMARTCOMP), 2020-9-14 - 2020-9-17.
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Abstract
Social media platforms such as Twitter are increasingly used to collect data of all kinds. During natural disasters, users may post text and image data on social media platforms to report information about infrastructure damage, injured people, cautions and warnings. Effective processing and analysing tweets in real time can help city organisations gain situational awareness of the affected citizens and take timely operations. With the advances in deep learning techniques, recent studies have significantly improved the performance in classifying crisis-related tweets. However, deep learning models are vulnerable to adversarial examples, which may be imperceptible to the human, but can lead to model's misclassification. To process multi-modal data as well as improve the robustness of deep learning models, we propose a multi-modal adversarial training method for crisis-related tweets classification in this paper. The evaluation results clearly demonstrate the advantages of the proposed model in improving the robustness of tweet classification.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | Adversarial training, Crisis-related data classification, Convolutional neural network, Smart city, Deep learning |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 11 Sep 2020 07:48 |
| Last Modified: | 14 Mar 2024 21:44 |
| DOI: | 10.1109/SMARTCOMP50058.2020.00051 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3100681 |
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