Adaptation Method for Misinformation Identification
Journal:
arXiv
Published Date:
Apr 19, 2025
Abstract
Multimodal fake news detection plays a crucial role in combating online
misinformation. Unfortunately, effective detection methods rely on annotated
labels and encounter significant performance degradation when domain shifts
exist between training (source) and test (target) data. To address the
problems, we propose ADOSE, an Active Domain Adaptation (ADA) framework for
multimodal fake news detection which actively annotates a small subset of
target samples to improve detection performance. To identify various deceptive
patterns in cross-domain settings, we design multiple expert classifiers to
learn dependencies across different modalities. These classifiers specifically
target the distinct deception patterns exhibited in fake news, where two
unimodal classifiers capture knowledge errors within individual modalities
while one cross-modal classifier identifies semantic inconsistencies between
text and images. To reduce annotation costs from the target domain, we propose
a least-disagree uncertainty selector with a diversity calculator for selecting
the most informative samples. The selector leverages prediction disagreement
before and after perturbations by multiple classifiers as an indicator of
uncertain samples, whose deceptive patterns deviate most from source domains.
It further incorporates diversity scores derived from multi-view features to
ensure the chosen samples achieve maximal coverage of target domain features.
The extensive experiments on multiple datasets show that ADOSE outperforms
existing ADA methods by 2.72\% $\sim$ 14.02\%, indicating the superiority of
our model.
