Multimodal Fake News Video Explanation: Dataset, Analysis and Evaluation

• URL: http://arxiv.org/abs/2501.08514v3
• Date: Thu, 17 Apr 2025 15:16:30 GMT
• Title: Multimodal Fake News Video Explanation: Dataset, Analysis and Evaluation
• Authors: Lizhi Chen, Zhong Qian, Peifeng Li, Qiaoming Zhu,
• Abstract summary: We develop a new dataset of 2,672 fake news video posts that can definitively explain four real-life fake news video aspects.<n>In addition, we propose a Multimodal Relation Graph Transformer (MRGT) based on the architecture of multimodal Transformer to benchmark FakeVE.
• Score: 13.779579002878918
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal fake news videos are difficult to interpret because they require comprehensive consideration of the correlation and consistency between multiple modes. Existing methods deal with fake news videos as a classification problem, but it's not clear why news videos are identified as fake. Without proper explanation, the end user may not understand the underlying meaning of the falsehood. Therefore, we propose a new problem - Fake news video Explanation (FNVE) - given a multimodal news post containing a video and title, our goal is to generate natural language explanations to reveal the falsity of the news video. To that end, we developed FakeVE, a new dataset of 2,672 fake news video posts that can definitively explain four real-life fake news video aspects. In order to understand the characteristics of fake news video explanation, we conducted an exploratory analysis of FakeVE from different perspectives. In addition, we propose a Multimodal Relation Graph Transformer (MRGT) based on the architecture of multimodal Transformer to benchmark FakeVE. The empirical results show that the results of the various benchmarks (adopted by FakeVE) are convincing and provide a detailed analysis of the differences in explanation generation of the benchmark models.

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