Hierarchical Graph-based Contrastive Adversarial Learning for Multimodal Fake News Detection
Authors: Guoyi Li, Zhongjiang Yao, Yingrui Xu, Yulei Wu, Xiaodan Zhang, Honglei Lyu, Wei Mi


Reviewer paper summaries: This paper focuses on the robustness issue in multimodal fake news detection models and proposes the Hierarchical Graph-based Contrastive Alignment Learning (HGCAL) model. HGCAL utilizes a hierarchical graph structure coupled with contrastive learning to achieve multi-granularity alignment of contextual representations. This approach enhances the robustness of the detector by facilitating modal alignment and employing a dual discriminator strategy. Extensive experiments confirm the model's superior detection capabilities and robustness.
Summary of strengths: 1. This article targets on a practical problem of fake news detection, which is of significant importance in the real world.
2. The authors provide extensive experimental results to back up the effectiveness of their model.
Summary of weaknesses: 1. The discussion on how current detection methods fail under certain adversarial attacks is unclear. It would be helpful to include specific examples of these attacks, discuss their likelihood in real-life situations, and explain in which scenarios HGCAL performs better or might still be limited.
2. Some fundamental details of the methodology are not sufficiently explained, such as the strategy for constructing positive and negative sample pairs in contrastive learning.
Comments, suggestions and typos: 1. The paper focuses on comparing the robustness of HGCAL with baseline models. It would be insightful to explore further which specific structure of HGCAL contributes to its robustness.
2. There are some unfinished details in the appendix (e.g., line 1035, line 1079) that require completion or clarification.
Overall assessment scores: 2.5