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Social media platforms have revolutionized human interaction, creating dynamic environments where millions of users exchange information, form communities, and influence one another. These platforms, including X and Reddit, are not just tools for communication but have become critical ecosystems for understanding modern societal behaviors. Simulating such intricate interactions is vital for studying misinformation, group polarization, and herd behavior. Computational models provide researchers a cost-effective and scalable way to analyze these interactions without conducting resource-intensive real-world experiments. But, creating models replicating the scale and complexity of social networks remains a significant challenge.The primary issue in modeling social media is capturing millions of users diverse behaviors and interactions in a dynamic network. Traditional agent-based models (ABMs) fall short of representing complex behaviors like context-driven decision-making or the influence of dynamic recommendation algorithms. Also, existing models are often limited to small-scale simulations, typically involving only hundreds or thousands of agents, which restricts their ability to mimic large-scale social systems. Such constraints hinder researchers from fully exploring phenomena like how misinformation spreads or how group dynamics evolve in online environments. These limitations highlight the need for more advanced and scalable simulation tools.Existing methods for simulating social media interactions often lack essential features like dynamic user networks, detailed recommendation systems, and real-time updates. For instance, most ABMs rely on pre-programmed agent behaviors, which fail to reflect the nuanced decision-making seen in real-world users. Also, current simulators are typically platform-specific, designed to study isolated phenomena, making them impractical for broader applications. They cannot often scale beyond a few thousand agents, leaving researchers unable to examine the behaviors of millions of users interacting simultaneously. The absence of scalable, versatile models has been a major bottleneck in advancing social media research.Researchers from Camel-AI, Shanghai Artificial Intelligence Laboratory, Dalian University of Technology, Oxford, KAUST, Fudan University, Xian Jiaotong University, Imperial College London, Max Planck Institute, and The University of Sydney developed OASIS, a next-generation social media simulator designed for scalability and adaptability to address these challenges. OASIS is built upon modular components, including an Environment Server, Recommendation System (RecSys), Time Engine, and Agent Module. It supports up to one million agents, making it one of the most comprehensive simulators. This system incorporates dynamically updated networks, diverse action spaces, and advanced algorithms to replicate real-world social media dynamics. By integrating data-driven methods and open-source frameworks, OASIS provides a flexible platform for studying phenomena across platforms like X and Reddit, enabling researchers to explore topics ranging from information propagation to herd behavior.The architecture of OASIS emphasizes both scale and functionality. The functions of some of the components are as follows:Its Environment Server is the backbone, storing detailed user profiles, historical interactions, and social connections.The Recommendation System customizes content visibility using advanced algorithms such as TwHIN-BERT, which processes user interests and recent activities to rank posts.The Time Engine governs user activation based on hourly probabilities, simulating realistic online behavior patterns.These components work together to create a simulation environment that can adapt to different platforms and scenarios. Switching from X to Reddit requires minimal module adjustments, making OASIS a versatile tool for social media research. Its distributed computing infrastructure ensures efficient handling of large-scale simulations, even with up to one million agents.In experiments modeling information propagation on X, OASIS achieved a normalized RMSE of approximately 30%, demonstrating its ability to align with actual dissemination trends. The simulator also replicated group polarization, showing that agents tend to adopt more extreme opinions during interactions. This effect was particularly pronounced in uncensored models, where agents used more extreme language. Moreover, OASIS revealed unique insights, such as the herd effect being more evident in agents than in humans. Agents consistently followed negative trends when exposed to down-treated comments, while humans displayed a stronger critical approach. These findings underscore the simulators potential to uncover both expected and novel patterns in social behavior.With OASIS, larger agent groups lead to richer and more diverse interactions. For example, when the number of agents increased from 196 to 10,196, the diversity and helpfulness of user responses improved significantly, with a 76.5% increase in perceived helpfulness. At an even larger scale of 100,196 agents, user interactions became more varied and meaningful, illustrating the importance of scalability in studying group behavior. Also, OASIS demonstrated that misinformation spreads more effectively than truthful information, particularly when rumors are emotionally provocative. The simulator also showed how isolated user groups form over time, providing valuable insights into the dynamics of online communities.Key takeaways from the OASIS research include:OASIS can simulate up to one million agents, far surpassing the capabilities of existing models.It supports multiple platforms, including X and Reddit, with modular components that are easily adjustable.The simulator replicates phenomena like group polarization and herd behavior, providing a deeper understanding of these dynamics.OASIS achieved a normalized RMSE of 30% in information propagation experiments, closely aligning with real-world trends.It demonstrated that rumors spread faster and more widely than truthful information in large-scale simulations.Larger agent groups enhance the diversity and helpfulness of responses, emphasizing the importance of scale in social media studies.OASIS distributed computing allows for efficient handling of simulations, even with millions of agents.In conclusion, OASIS is a breakthrough in simulating social media dynamics, offering scalability and adaptability. OASIS addresses existing model limitations and provides a robust framework for studying complex-scale interactions. Integrating LLMs with rule-based agents accurately mimics the behaviors of up to one million users across platforms like X and Reddit. Its ability to replicate complex phenomena, such as information propagation, group polarization, and herd effects, provides researchers valuable insights into modern social ecosystems.Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our60k+ ML SubReddit. Sana Hassan+ postsSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions. 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