Summary of Finefake: a Knowledge-enriched Dataset For Fine-grained Multi-domain Fake News Detection, by Ziyi Zhou et al.
FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection
by Ziyi Zhou, Xiaoming Zhang, Litian Zhang, Jiacheng Liu, Senzhang Wang, Zheng Liu, Xi Zhang, Chaozhuo Li, Philip S. Yu
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 The proposed paper introduces a novel multi-domain knowledge-enhanced benchmark for fake news detection, called FineFake. This benchmark addresses the limitations of existing benchmarks by encompassing 16,909 data samples spanning six semantic topics and eight platforms, with each news item enriched with multi-modal content, potential social context, semi-manually verified common knowledge, and fine-grained annotations that surpass conventional binary labels. The paper also formulates three challenging tasks based on FineFake and proposes a knowledge-enhanced domain adaptation network to tackle these tasks. Extensive experiments are conducted on FineFake under various scenarios, providing accurate and reliable benchmarks for future endeavors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to detect fake news that takes into account the diversity of real-world news. It creates a big database with many different types of news articles and labels them as true or false. The database includes information like what’s happening on social media, common knowledge, and more. This helps machines learn to recognize patterns in fake news and distinguish it from real news. |
Keywords
» Artificial intelligence » Domain adaptation » Multi modal
