# Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks

arXiv: https://arxiv.org/abs/2307.10337 · HTML: https://mlsanity.com/paper/2307.10337/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2023.

## Built on by (most cited first, 13 of 13)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Do LLM Agents Have Regret? A Case Study in Online Learning and Games](https://mlsanity.com/paper/2403.16843/) (2024)
- [Large Language Models for Manufacturing](https://mlsanity.com/paper/2410.21418/) (2024)
- [Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation](https://mlsanity.com/paper/2404.09043/) (2024)
- [StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving](https://mlsanity.com/paper/2311.08803/) (2023)
- [Language Evolution for Evading Social Media Regulation via LLM-based Multi-agent Simulation](https://mlsanity.com/paper/2405.02858/) (2024)
- [Chain-of-Trigger: An Agentic Backdoor that Paradoxically Enhances Agentic Robustness](https://mlsanity.com/paper/2510.08238/) (2025)
- [Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings](https://mlsanity.com/paper/2512.25055/) (2025)
- [Sleeper Social Bots: a new generation of AI disinformation bots are already a political threat](https://mlsanity.com/paper/2408.12603/) (2024)
- [Solving the Unsolvable: Translating Case Law in Hong Kong](https://mlsanity.com/paper/2501.09444/) (2025)
- [Key Decision-Makers in Multi-Agent Debates: Who Holds the Power?](https://mlsanity.com/paper/2511.11040/) (2025)

## Abstract (arXiv)

As the capabilities of Large Language Models (LLMs) emerge, they not only assist in accomplishing traditional tasks within more efficient paradigms but also stimulate the evolution of social bots. Researchers have begun exploring the implementation of LLMs as the driving core of social bots, enabling more efficient and user-friendly completion of tasks like profile completion, social behavior decision-making, and social content generation. However, there is currently a lack of systematic research on the behavioral characteristics of LLMs-driven social bots and their impact on social networks. We have curated data from Chirper, a Twitter-like social network populated by LLMs-driven social bots and embarked on an exploratory study. Our findings indicate that: (1) LLMs-driven social bots possess enhanced individual-level camouflage while exhibiting certain collective characteristics; (2) these bots have the ability to exert influence on online communities through toxic behaviors; (3) existing detection methods are applicable to the activity environment of LLMs-driven social bots but may be subject to certain limitations in effectiveness. Moreover, we have organized the data collected in our study into the Masquerade-23 dataset, which we have publicly released, thus addressing the data void in the subfield of LLMs-driven social bots behavior datasets. Our research outcomes provide primary insights for the research and governance of LLMs-driven social bots within the research community.