Authors: Xiaofei Xu, Ke Deng, Michael Dann, Xiuzhen Zhang

Published on: January 28, 2024

Impact Score: 8.2

Arxiv code: Arxiv:2402.03357

Summary

• What is new: NAGASIL approach includes learning from negative samples and an augmented state representation for effective fake news mitigation.
• Why this is important: Minimizing the impact of fake news on social networks by effectively deploying debunkers.
• What the research proposes: NAGASIL – Negative sampling and state Augmented Generative Adversarial Self-Imitation Learning for learning a more efficient debunker selection policy.
• Results: Superior performance in fake news mitigation on two social networks compared to standard GASIL and other leading methods.

Technical Details

Technological frameworks used: Reinforcement learning, specifically using Self-Imitation Learning (SIL)

Models used: Generative Adversarial Self-Imitation Learning (GASIL) with NAGASIL improvements

Data used: nan

Potential Impact

Social media platforms, news organizations, and companies involved in information verification technology.

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