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

arXiv: https://arxiv.org/abs/2409.11426 · HTML: https://mlsanity.com/paper/2409.11426/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/)

## Builds on (most cited first, 7 of 7)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Emergence of polarized ideological opinions in multidimensional topic spaces](https://mlsanity.com/paper/2007.00601/) (2020)
- [Predicting Opinion Dynamics via Sociologically-Informed Neural Networks](https://mlsanity.com/paper/2207.03990/) (2022)
- [Opinion shaping in social networks using reinforcement learning](https://mlsanity.com/paper/1910.08802/) (2019)
- [Convergence Properties of the Heterogeneous Deffuant-Weisbuch Model](https://mlsanity.com/paper/1901.02092/) (2019)
- [Shifting Opinions in a Social Network Through Leader Selection](https://mlsanity.com/paper/1910.13009/) (2019)
- [Polarizing Political Polls: How Visualization Design Choices Can Shape Public Opinion and Increase Political Polarization](https://mlsanity.com/paper/2309.00690/) (2023)

## Built on by (most cited first, 1 of 1)

- [Synthetic Social Media Influence Experimentation via an Agentic Reinforcement Learning Large Language Model Bot](https://mlsanity.com/paper/2411.19635/) (2024)

## Abstract (arXiv)

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.