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 (CANMD). Specifically, we leverage pseudo labeling to generate
high-confidence target examples for joint training with source data. We
additionally design a label correction component to estimate and correct the
label shifts (i.e., class priors) between the source and target domains.
Moreover, a contrastive adaptation loss is integrated in the objective function
to reduce the intra-class discrepancy and enlarge the inter-class discrepancy.
As such, the adapted model learns corrected class priors and an invariant
conditional distribution across both domains for improved estimation of the
target data distribution. To demonstrate the effectiveness of the proposed
CANMD, we study the case of COVID-19 early misinformation detection and perform
extensive experiments using multiple real-world datasets. The results suggest
that CANMD can effectively adapt misinformation detection systems to the unseen
COVID-19 target domain with significant improvements compared to the
state-of-the-art baselines.

本文提出了一种用于早期误报检测（CANMD）的对比适应网络，它通过伪标记和标签纠正组件相结合，有效地适应了未知 COVID-19 目标领域，与现有技术相比，取得了显著的改进。

基于对比领域自适应的早期虚假信息检测：以 COVID-19 为例

Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain
data while manual annotations are only available in the source domain. Previous
methods minimize the domain discrepancy neglecting the class information, which
may lead to misalignment and poor generalization performance. To address this
issue, this paper proposes Contrastive Adaptation Network (CAN) optimizing a
new metric which explicitly models the intra-class domain discrepancy and the
inter-class domain discrepancy. We design an alternating update strategy for
training CAN in an end-to-end manner. Experiments on two real-world benchmarks
Office-31 and VisDA-2017 demonstrate that CAN performs favorably against the
state-of-the-art methods and produces more discriminative features.

本文章提出了一种基于对比适应性网络 (CAN) 的无监督域自适应方法来解决类别信息不足导致的领域差异偏差问题，并证明 CAN 在两个真实场景 Office-31 和 VisDA-2017 上表现优异，具有更好的特征鉴别能力。