Towards Opinion Shaping: A Deep Reinforcement Learning Approach in Bot-User Interactions
This addresses the challenge of optimizing advertising and influence strategies on social platforms, though it appears incremental as it applies existing DRL methods to a specific model.
The paper tackles the problem of opinion shaping in social networks by using deep reinforcement learning to control bots and targeted advertising within the Stochastic Bounded Confidence Model, showing that this approach leads to efficient opinion shaping.
This paper aims to investigate the impact of interference in social network algorithms via user-bot interactions, focusing on the Stochastic Bounded Confidence Model (SBCM). This paper explores two approaches: positioning bots controlled by agents into the network and targeted advertising under various circumstances, operating with an advertising budget. This study integrates the Deep Deterministic Policy Gradient (DDPG) algorithm and its variants to experiment with different Deep Reinforcement Learning (DRL). Finally, experimental results demonstrate that this approach can result in efficient opinion shaping, indicating its potential in deploying advertising resources on social platforms.