Multi-View Consistency Learning with Collaborative Large and Small Models for Short Video Fake News Detection
Authors: Linlin Zong, Wenmin Lin, Jiahui Zhou, Shimin Wu, Xinyue Liu, Xianchao Zhang, Bo Xu


Reviewer paper summaries: This paper proposes Multi-View Consistency Learning with Collaborative Large and Small models (MVC-CLS) for short video fake news detection. It considers sentiment and content consistency as key indicators for identifying fake information.

This paper proposes the MVC-CLS method, which leverages Large Language Models (LLM) and small multi-modal models to analyze sentiment and content consistency in short video news for improved fake news detection. Experimental results on the FakeSV dataset demonstrate that this method significantly outperforms existing state-of-the-art methods.
Summary of strengths: 1. The article addresses the practical issue of fake news detection, which holds significant real-world importance.
2. The paper is well-structured, and the overall writing quality is satisfactory.

S1: Innovative multi-view consistency analysis method combining the strengths of LLM and small multi-modal models on short video fake news detection.

S2: The method demonstrates significant performance improvement on the FakeSV dataset compared to existing methods.
Summary of weaknesses: 1. The novelty of the article is questionable. Leveraging large language models for additional analysis[1], as well as using sentiment[2] and content consistency[3] to enhance fake news detection, have been explored in previous works. It appears that the authors have merely combined these existing ideas without introducing significant innovation.
2. The distinction between consistency analysis by large models and small models is not clear. How do they complement each other? The authors should provide specific cases to illustrate this.
3.  The study uses only one dataset. It would be beneficial to include comparative analyses on additional datasets to validate the findings.

[1]  Hu, Beizhe, et al. "Bad actor, good advisor: Exploring the role of large language models in fake news detection." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 20. 2024. 

[2]  Zhang, Xueyao, et al. "Mining dual emotion for fake news detection." Proceedings of the web conference 2021. 2021. 

[3]  Xue, Junxiao, et al. "Detecting fake news by exploring the consistency of multimodal data." Information Processing & Management 58.5 (2021): 102610.

W1: The rationality of certain data processing procedures needs further discussion.
(1) In Section 3.2, applying a weighting method to comment features based on the number of likes seems inconsistent with the formula provided, which primarily weights comments based on their indexes. 
(2) In Section 3.4.1, sentiment analysis of the entire comment data is averaged, without considering the potential need for weighting based on the number of likes.

W2: Limited consistency analysis.
(1) The paper claims in its contribution summary, "We propose a thorough analysis of sentiment and content consistency across multiple modalities," but it is not comprehensive. For instance, there is no analysis of the consistency of content and sentiment in video and audio, nor is there any utilization of visual information from the video. 
(2) In Section 3.3, the use of large models mainly focuses on textual consistency analysis. It is suggested to leverage the multi-modal capabilities of large models to analyze consistency from more perspectives.

W3: Insufficient and incomplete descriptions in the paper.
(1) In the introduction, the statement "These relationships are crucial, as fake news often violates sentiment and content consistency across multiple modalities" lists sentiment and content consistency in one order, but this order is reversed later in the description.
(2) In the last paragraph of Section 3.1, it is mentioned, "The MVC-CLS model is divided into four modules," but only three modules are introduced in this section, omitting the Small multi-modal consistency analysis model.
(3) In Section 3.5, the specific implementation details of feature fusion and classification are not described in detail. For example, the exact operational steps for feature fusion and the specific structure of the fully connected layer are missing.
Comments, suggestions and typos: Please refer to the weaknesses section.

See the summary above.
Overall assessment scores: 2

1.5