Summary of Official-nv: An Llm-generated News Video Dataset For Multimodal Fake News Detection, by Yihao Wang et al.
Official-NV: An LLM-Generated News Video Dataset for Multimodal Fake News Detection
by Yihao Wang, Lizhi Chen, Zhong Qian, Peifeng Li
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the pressing issue of fake news detection in video media, a growing concern with the proliferation of online news sources. The existing datasets for this task are plagued by superfluous data, which hinders model training. To address this problem, the authors create the Official-NV dataset, comprising officially published news videos. This dataset is augmented using language models (LLMs) and manual verification to increase its size. The researchers also propose a novel baseline model called OFNVD, which employs attention mechanisms and cross-modal Transformers to analyze multimodal features and enhance feature extraction. Benchmarking experiments demonstrate the effectiveness of the proposed model in detecting fake news in video media. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake news is a big problem in today’s world, especially with online videos. Right now, there aren’t many good ways to detect fake news in videos because the data sets used to train these detectors are too messy. To fix this, researchers created a new dataset called Official-NV that only includes official news videos. They also made it bigger by using special language models and checking everything by hand. The team came up with a new way to analyze video information called OFNVD, which is better at detecting fake news than previous methods. |
Keywords
* Artificial intelligence * Attention * Feature extraction
