利用网络效应缓解假新闻：通过自模仿学习选择辟谣者
Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning
AI总结：
本研究提出NAGASIL方法，通过负采样和状态增强的自模仿学习，在社交网络上选择辟谣者传播真实新闻以缓解假新闻，实验证明其性能优于现有模型。
摘要：
本研究旨在通过在社交网络上部署辟谣者传播真实新闻，以最小化假新闻的影响。该问题被构建为一个强化学习问题，在每个阶段，选择一个用户来传播真实新闻。一个具有挑战性的问题是情节奖励，其中选择单个辟谣者的“净”效应无法从社交网络上交错的信息传播中辨别出来，只能观察到缓解努力产生的集体效应。现有的自模仿学习（SIL）方法在从情节奖励中学习方面显示出潜力，但由于其样本效率低下，不适合假新闻缓解的实际应用。为了学习更有效的辟谣者选择策略以缓解假新闻，本研究提出了NAGASIL——负采样和状态增强的生成对抗自模仿学习，它包含两项针对假新闻缓解的改进：从负样本中学习，以及通过将当前观察到的状态与来自同一活动的先前状态-动作对整合来增强状态表示，以捕捉“真实”的环境状态。在两个社交网络上的实验表明，NAGASIL的性能优于标准GASIL和最先进的假新闻缓解模型。
英文摘要：
This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news. This is framed as a reinforcement learning problem, where, at each stage, one user is selected to propagate true news. A challenging issue is episodic reward where the "net" effect of selecting individual debunkers cannot be discerned from the interleaving information propagation on social networks, and only the collective effect from mitigation efforts can be observed. Existing Self-Imitation Learning (SIL) methods have shown promise in learning from episodic rewards, but are ill-suited to the real-world application of fake news mitigation because of their poor sample efficiency. To learn a more effective debunker selection policy for fake news mitigation, this study proposes NAGASIL - Negative sampling and state Augmented Generative Adversarial Self-Imitation Learning, which consists of two improvements geared towards fake news mitigation: learning from negative samples, and an augmented state representation to capture the "real" environment state by integrating the current observed state with the previous state-action pairs from the same campaign. Experiments on two social networks show that NAGASIL yields superior performance to standard GASIL and state-of-the-art fake news mitigation models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
