TY - GEN
T1 - Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup
AU - Zeng, Huimin
AU - Yue, Zhenrui
AU - Kou, Ziyi
AU - Shang, Lanyu
AU - Zhang, Yang
AU - Wang, Dong
N1 - ACKNOWLEDGMENTS This research is supported in part by the National Science Foundation under Grant No. IIS-2202481, CHE-2105005, IIS-2008228, CNS-1845639, CNS-1831669. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
PY - 2022
Y1 - 2022
N2 - In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) based discrepancy between these two domains. Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process. Extensive experiments on multiple real-world datasets suggest that our method can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines.
AB - In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) based discrepancy between these two domains. Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process. Extensive experiments on multiple real-world datasets suggest that our method can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines.
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U2 - 10.1109/ASONAM55673.2022.10068580
DO - 10.1109/ASONAM55673.2022.10068580
M3 - Conference contribution
AN - SCOPUS:85151984530
T3 - Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
SP - 159
EP - 162
BT - Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
A2 - An, Jisun
A2 - Charalampos, Chelmis
A2 - Magdy, Walid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
Y2 - 10 November 2022 through 13 November 2022
ER -