Text-Conditioned Multimodal Alignment and Consistency Modeling for Fake News Video Detection
Authors: Bohan Qi, Ruohan Wang, Chao Gao, Xianghua Li, Zhen Wang


Reviewer paper summaries: This paper proposes to reduce noise by aligning video titles with multimodal features to extract key information relevant to the title for improved fake news video detection. The model's performance is validated through experiments on one benchmark dataset.
Summary of strengths: 1. The article addresses the practical issue of fake news detection, which is of significant real-world importance.
2. The paper is well-structured, and the overall writing quality is satisfactory.
Summary of weaknesses: 1. The authors claim their method aims to eliminate the effect of noise but fail to clearly define what constitutes noise. Not all parts irrelevant to the title in different modalities can be considered noise. The authors should provide a more detailed and clear explanation of the rationale behind using text-conditioned alignment for noise reduction.
2. The experimental design lacks clarity in terms of what the authors aim to verify. Some experiments, such as comparisons with unimodal baselines, lack significance. Additionally, they missed necessary comparative experiments with other common methods aimed at eliminating noise to improve classification performance.
3. The study is limited to one dataset. It would be beneficial to include comparative analyses on additional datasets.
Comments, suggestions and typos: Please refer to the part of Weaknesses.
Overall assessment scores: 2