Multi-agent Deep Reinforcement Learning for Fake News Detection

With the rise of social media, fake news has been known to spread rapidly through the platforms and significantly influencing public opinion. However, traditional machine learning techniques are difficult in addressing fake news detection problem due to the highly imbalanced distribution of classes. As an alternative, reinforcement learning (RL) can be used to address the imbalanced prediction problem by designing different reward functions. However, it ignores the dynamic and adversarial environment in social networks, where malicious user can influence the platform’s decision. To address these challenges, we propose a multi-agent reinforcement learning (MARL) framework called Information Classification Markov Game (ICMG) to model fake news detection as a competitive game between a malicious user and a content moderator. Within this framework, we explore the best reward function for a content moderator to achieve better performance in a real world dataset. Additionally, we compare the effectiveness of different types of agents including those based on randomness, Minimax and Q-Learning. Our results show that a MARL setting with both players using Q-Learning setting achieves the best performance. Compared with the single-agent framework, ICMG effectively improves macro F1 score, improving the model performance on the real-world dataset. Moreover, this framework effectively simulates the adversarial dynamics in real-world social media platforms, providing a new approach for developing more realistic content moderation systems.