TY - GEN
T1 - Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media
AU - Chen, Qi
AU - Wang, Wei
AU - Huang, Kaizhu
AU - De, Suparna
AU - Coenen, Frans
N1 - Funding Information:
This research is funded by the Research Development Fund at Xi’an Jiaotong-Liverpool University, contract number RDF-16-01-34.
Funding Information:
This research is funded by the Research Development Fund at Xi'an Jiaotong-Liverpool University, contract number RDF-16-01-34.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Adversarial training
KW - Convolutional neural network
KW - Crisis-related data classification
KW - Deep learning
KW - Smart city
UR - https://www.scopus.com/pages/publications/85097333629
U2 - 10.1109/SMARTCOMP50058.2020.00051
DO - 10.1109/SMARTCOMP50058.2020.00051
M3 - Conference Proceeding
AN - SCOPUS:85097333629
T3 - Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020
SP - 232
EP - 237
BT - Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Smart Computing, SMARTCOMP 2020
Y2 - 14 September 2020 through 17 September 2020
ER -