Summary of Reliability Estimation Of News Media Sources: Birds Of a Feather Flock Together, by Sergio Burdisso et al.
Reliability Estimation of News Media Sources: Birds of a Feather Flock Together
by Sergio Burdisso, Dairazalia Sánchez-Cortés, Esaú Villatoro-Tello, Petr Motlicek
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 approach leverages reinforcement learning strategies to estimate the reliability degree of news sources by modeling how all media sources interact with each other on the Web. Unlike previous research, this method estimates a reliability degree rather than a label. The authors validated their method on a large-scale dataset and achieved strong correlations with journalist-provided scores (Spearman=0.80) and accurate prediction of reliability labels (macro-avg. F1 score=81.05). The implementation and dataset are released to support the NLP community working on information verification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps figure out which news sources are trustworthy or not. This is important for journalists and organizations that want to spread accurate information. The authors came up with a new way to do this using something called reinforcement learning. They tested their method on a really big dataset and it worked well, matching what experts thought about the reliability of each source. They’re sharing their code and data so others can use it to help fight fake news. |
Keywords
» Artificial intelligence » F1 score » Nlp » Reinforcement learning
