Leveraging Multimodal Fusion for Advanced Fake News Detection
Authors: Yang Xu, Shujun Li, Jie Guo, Weidong Qiu, Zheng Huang


Reviewer paper summaries: The global and local features of the text are considered in this article, and visual information and metadata information are integrated to realize multimodal fake news detection, and advanced performance is achieved on Fakeddit dataset. However, this article also has some shortcomings, including the model design is relatively simple, only global and local semantic information is added, it ignores some important structural introductions in the methodology section and the experiment is only verified on Fakeddit dataset, lacking universal persuasion.
Summary of strengths: 1) This article presents a new approach to solving the multimodal fake news detection task.
2) The model structure in this article can clearly show the main framework of the model, and based on this, we can fully understand the process of multimodal feature extraction and fusion, especially the local and global feature extraction of text modality.
3) This article makes a case study, enumerates the classification in Fakeddit data set, and objectively analyzes limitation and future work.
Summary of weaknesses: 1) Poor innovation. This article mentioned "most focus solely on the original text and images, paying insurance attention to associated comments and metadata.", but in fact, the technology of metadata detection has been studied by scholars, and the Introduction part lacks the advancement of this research.
2) Meta-feature extraction. In the process of "meta-information", you use standardized and multi-layer perceptron to obtain meta-features. This way pays attention to quantitative information. Whether there is any reference to prove that simple quantitative information is related to fake nature, please explain it. The introduction of meta-feature extraction in Methodology is omitted.
3) Insufficient experiment. Designing experiments on only one data set to achieve outstanding results is not effective and convincing.
4) Inadequate baselines. At present, there are literature on research results on the direction of the future work part, which need to be further updated.
Comments, suggestions and typos: 1) In the fusion part of Section 3.2, a number of widely used fusion strategies are introduced in general, which has little correlation with this model, and the introduction of the fusion method adopted in this model is simple.
2) Many achievements have been made in this field in 2022-2023, but the references in this article focus on 2019 and before, which need to be updated.
3) The pictures in this paper are not of high resolution.
Overall assessment scores: 2