Summary of Each Fake News Is Fake in Its Own Way: An Attribution Multi-granularity Benchmark For Multimodal Fake News Detection, by Hao Guo et al.
Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection
by Hao Guo, Zihan Ma, Zhi Zeng, Minnan Luo, Weixin Zeng, Jiuyang Tang, Xiang Zhao
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel multimodal fake news detection system is proposed to tackle the issue of misinformation on social platforms. Existing datasets only provide binary labels, neglecting the diverse nature of fake news. A new dataset, AMG, is constructed to reflect this complexity, featuring an attribution setting that reveals the inherent patterns of fake news. The authors also introduce a Multi-Granularity Clue Alignment Model (our) to detect and attribute multimodal fake news. Experimental results show that AMG is a challenging dataset, opening up opportunities for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake news detection on social media is important because it can lead to negative consequences. Right now, there are only two types of labels: real or fake. But this doesn’t accurately represent the many different kinds of fake news out there. To fix this, researchers created a new dataset called AMG that shows how each piece of fake news is unique in its own way. They also developed a special model to detect and understand this kind of fake news. This can help us figure out ways to stop it from spreading. |
Keywords
» Artificial intelligence » Alignment
