  In this paper we introduce Y, a new-generation digital twin designed to
replicate an online social media platform. Digital twins are virtual replicas
of physical systems that allow for advanced analyses and experimentation. In
the case of social media, a digital twin such as Y provides a powerful tool for
researchers to simulate and understand complex online interactions. {\tt Y}
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, Y offers valuable insights into user engagement, information spread,
and the impact of platform policies. Moreover, the integration of LLMs allows Y
to generate nuanced textual content and predict user responses, facilitating
the study of emergent phenomena in online environments.
  To better characterize the proposed digital twin, in this paper we describe
the rationale behind its implementation, provide examples of the analyses that
can be performed on the data it enables to be generated, and discuss its
relevance for multidisciplinary research.
