Towards Opinion Shaping: A Deep Reinforcement Learning Approach in Bot-User Interactions

Autor:	Siahkali, Farbod, Samadi, Saba, Kebriaei, Hamed
Rok vydání:	2024
Předmět:	Computer Science - Social and Information Networks Computer Science - Artificial Intelligence Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control I.2.8 I.2.6
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 3 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.11426 Zobrazit plný text záznamu View this record from Arxiv