Towards Opinion Shaping: A Deep Reinforcement Learning Approach in Bot-User Interactions
Social and Information Networks
2024-09-19 v1 Artificial Intelligence
Machine Learning
Systems and Control
Systems and Control
Abstract
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.
Cite
@article{arxiv.2409.11426,
title = {Towards Opinion Shaping: A Deep Reinforcement Learning Approach in Bot-User Interactions},
author = {Farbod Siahkali and Saba Samadi and Hamed Kebriaei},
journal= {arXiv preprint arXiv:2409.11426},
year = {2024}
}
Comments
5 pages, 3 figures, 2 tables