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This paper introduces YSocial, a new-generation virtual twin designed to replicate an online social media platform. Digital and virtual twins are virtual replicas of physical systems that allow for advanced analyses, control, experimentation, and scenario simulations. In the case of social media, a virtual twin such as YSocial provides a powerful tool for researchers to simulate and understand complex online interactions. YSocial leverages state-of-the-art large language models (LLMs) to replicate sophisticated agent behaviors, enabling accurate simulations of user interactions, content dissemination, and network dynamics. By integrating these aspects, YSocial offers valuable insights into user engagement, information spread, and the impact of platform policies. Moreover, integrating LLMs allows YSocial to generate nuanced textual content and predict user responses, facilitating the study of emergent phenomena in online environments. To better characterize YSocial, we describe the rationale behind its implementation, provide examples of the analyses that can be performed on the data it generates, and discuss its relevance for multidisciplinary research.
Rossetti et al. (Sat,) studied this question.