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Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media

Publication ,  Conference
Chen, Q; Wang, W; Huang, K; De, S; Coenen, F
Published in: Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020
September 1, 2020

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.

Duke Scholars

Published In

Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020

DOI

ISBN

9781728169972

Publication Date

September 1, 2020

Start / End Page

232 / 237
 

Citation

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Chen, Q., Wang, W., Huang, K., De, S., & Coenen, F. (2020). Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media. In Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020 (pp. 232–237). https://doi.org/10.1109/SMARTCOMP50058.2020.00051
Chen, Q., W. Wang, K. Huang, S. De, and F. Coenen. “Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media.” In Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020, 232–37, 2020. https://doi.org/10.1109/SMARTCOMP50058.2020.00051.
Chen Q, Wang W, Huang K, De S, Coenen F. Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media. In: Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020. 2020. p. 232–7.
Chen, Q., et al. “Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media.” Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020, 2020, pp. 232–37. Scopus, doi:10.1109/SMARTCOMP50058.2020.00051.
Chen Q, Wang W, Huang K, De S, Coenen F. Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media. Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020. 2020. p. 232–237.

Published In

Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020

DOI

ISBN

9781728169972

Publication Date

September 1, 2020

Start / End Page

232 / 237