Early Detection of Misinformation for Infodemic Management: A Domain Adaptation Approach

TL;DR

This paper introduces a novel domain adaptation method for early misinformation detection during infodemics, effectively addressing covariate and concept shifts to improve detection accuracy with limited labeled data.

Contribution

The paper provides a theoretical framework for handling both covariate and concept shifts and develops a new method that operationalizes this framework for misinformation detection.

Findings

Outperforms state-of-the-art misinformation detection methods

Effectively mitigates covariate and concept shift issues

Demonstrates superior performance on real-world datasets

Abstract

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.