Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation (2024)

Abstract: Social media has emerged as a cornerstone of social movements, wielding significant influence in driving societal change. Simulating the response of the public and forecasting the potential impact has become increasingly important. However, existing methods for simulating such phenomena encounter challenges concerning their efficacy and efficiency in capturing the behaviors of social movement participants. In this paper, we introduce a hybrid framework for social media user simulation, wherein users are categorized into two types. Core users are driven by Large Language Models, while numerous ordinary users are modeled by deductive agent-based models. We further construct a Twitter-like environment to replicate their response dynamics following trigger events. Subsequently, we develop a multi-faceted benchmark SoMoSiMu-Bench for evaluation and conduct comprehensive experiments across real-world datasets. Experimental results demonstrate the effectiveness and flexibility of our method.

## introduction in the past decades, social media has witnessed many social movements, such as the arab spring @xcite and #metoo @xcite . twitter stands out as a prominent forum giving powerful voices to groups demanding change. as illustrated in figure 1 , the dissemination of breaking news on twitter prompts the proliferation of opinions, influencing collective sentiment and shaping societal agendas, often resulting in real-world actions @xcite . although the majority of social movements are reported peaceful, the sheer scale of participation can sometimes escalate into violence and destruction, posing potential ramifica- highly active and passionate about sharing information and taking part … twitter timeline twitter timeline #blacklivematters he can not breathe! @user1: it's time for … message feed anna davis her/she anna is moderately active. she enjoys detailing and refining ideas … the officer was too cruel. we must … @johnsmith: #black… 𝑡 𝑡 + 1 … … figure 1 : an illustration of user interactions and attitude changes after a trigger event happens. users can take actions such as posting and retweeting according to their traits, and their generated content will be stored in the twitter timeline and fed to their connected users. users can change attitudes once perceive others' opinions. recently, large language models (llms) have demonstrated impressive ability in human-level intelligence @xcite . llm-based user simulations have been successfully experimented in domains such as recommendation @xcite and collaborative work @xcite . however, the exploration of conducting large-scale online social movement simulations using llms remains limited and presents the following challenges: (1) how to accurately simulate users of social media and replicate their behaviors within the community? (2) how to efficiently simulate a large number of users, given the impracticality of employing thousands of llms? (3) how to comprehensively evaluate the effectiveness of the simulation? to handle these challenges, this paper introduces a novel hybrid framework for social media user simulation. considering the pareto distribution foot_0 inherent in social media user engagement, we categorize users into two types: core users, comprising active and influential figures such as opinion leaders, and ordinary users. core users are characterized and driven by llms, enabling emulation of their complex behaviors, while a vast number of ordinary users are governed by abms, providing a practical way for user simulation in large scale. based on the hybrid mechanism composed of two types of users, we establish an online social media environment tailored for online social movement simulation and evaluation. in this environment, messages are organized in twitterlike timelines and offline news can be disseminated. user interactions and resultant collective attitudes are observed through attitude scores. to systematically evaluate the simulation, we propose a novel benchmark somosimu-bench, including three real-world collected datasets (metoo, roeoverturned and blacklivesmatter) and an evaluation strategy at both the micro and macro levels, focusing on individual user alignment and systemic outcomes respectively. evaluation results on somosimu-bench demonstrate the effectiveness of our simulation framework. our contributions can be summarized as follows: ## formalization of public opinion dynamics simulation modeling the change in people's attitudes, beliefs and opinions is crucial since opinion change can result in societal phenomena such as bipolarization and extremization. in this section, we present the preliminaries of agent-based models and the formulation of the targeted task. ## hybrid framework for social media simulation user engagement in social networks often exhibits a pareto distribution, where the bulk of content originates from a small fraction of individuals. thus, those more active and influential such as opinion leaders should be modeled finely, while the silent majority can be controlled by simpler models. the overall framework is illustrated in figure 2 , where social media users are divided into core users and ordinary users. the two types of users are driven by different models, to address the cost and efficiency issues of using thousands of llms. ## somosimu-bench: a benchmark for social movement simulation in this section, we present the somosimu-bench, a benchmark for simulation evaluation. we construct a data collection, composed of three social movements on twitter. then, evaluation strategies at the micro and macro levels are designed. ## conclusion in this paper, we propose a hybrid framework for social media user simulation. we empower core users and ordinary users with llms and abms, and we provide a twitter-like environment and a benchmark somosimu-bench for simulation and evaluation. experiment results demonstrate the effectiveness and flexibility of our method.