An infodemic refers to an enormous amount of true information and
misinformation disseminated during a disease outbreak. Detecting misinformation
at the early stage of an infodemic is key to manage it and reduce its harm to
public health. An early stage infodemic is characterized by a large volume of
unlabeled information concerning a disease. As a result, conventional
misinformation detection methods are not suitable for this misinformation
detection task because they rely on labeled information in the infodemic domain
to train their models. To address the limitation of conventional methods,
state-of-the-art methods learn their models using labeled information in other
domains to detect misinformation in the infodemic domain. The efficacy of these
methods depends on their ability to mitigate both covariate shift and concept
shift between the infodemic domain and the domains from which they leverage
labeled information. These methods focus on mitigating covariate shift but
overlook concept shift, rendering them less effective for the task. In
response, we theoretically show the necessity of tackling both covariate shift
and concept shift as well as how to operationalize each of them. Built on the
theoretical analysis, we develop a novel misinformation detection method that
addresses both covariate shift and concept shift. Using two real-world
datasets, we conduct extensive empirical evaluations to demonstrate the
superior performance of our method over state-of-the-art misinformation
detection methods as well as prevalent domain adaptation methods that can be
tailored to solve the misinformation detection task.

通过理论分析，我们开发了一种新的误信息检测方法，旨在解决 covariate shift 和 concept shift 问题，并通过广泛的实证评估证明了其在与先进的误信息检测方法和主要领域适应方法相比的卓越性能。