Sept. 1, 2022, 1:14 a.m. | Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang

cs.CL updates on arXiv.org arxiv.org

Despite recent progress in improving the performance of misinformation
detection systems, classifying misinformation in an unseen domain remains an
elusive challenge. To address this issue, a common approach is to introduce a
domain critic and encourage domain-invariant input features. However, early
misinformation often demonstrates both conditional and label shifts against
existing misinformation data (e.g., class imbalance in COVID-19 datasets),
rendering such methods less effective for detecting early misinformation. In
this paper, we propose contrastive adaptation network for early misinformation
detection …

arxiv case case study covid covid-19 detection domain adaptation misinformation study

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