Summary of Multi-view Fake News Detection Model Based on Dynamic Hypergraph, by Rongping Ye et al.
Multi-view Fake News Detection Model Based on Dynamic Hypergraph
by Rongping Ye, Xiaobing Pei
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Medium Difficulty summary: A novel dynamic hypergraph-based multi-view fake news detection model (DHy-MFND) is proposed to tackle the limitations of existing text-based and graph-based approaches. The model learns news embeddings across three distinct views: text-level, propagation tree-level, and hypergraph-level. This approach optimizes predefined hypergraph structures while learning news embeddings and captures authenticity-relevant embeddings through contrastive learning. The effectiveness of DHy-MFND is demonstrated on two benchmark datasets, outperforming a range of competing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: A new way to detect fake news online is developed. This method looks at news articles in three different ways: the words used, how the news spreads through people, and complex connections between many news stories. The approach learns about each news story by considering these different perspectives and captures important features that help distinguish real from fake news. Tests show that this method performs well on two large datasets of labeled news stories. |
