  Metaverse provides users with a novel experience through immersive multimedia
technologies. Along with the rapid user growth, numerous events bursting in the
metaverse necessitate an announcer to help catch and monitor ongoing events.
However, systems on the market primarily serve for esports competitions and
rely on human directors, making it challenging to provide 24-hour delivery in
the metaverse persistent world. To fill the blank, we proposed a three-stage
architecture for metaverse announcers, which is designed to identify events,
position cameras, and blend between shots. Based on the architecture, we
introduced a Metaverse Announcer User Experience (MAUE) model to identify the
factors affecting the users' Quality of Experience (QoE) from a human-centered
perspective. In addition, we implemented \textit{MetaCast}, a practical
self-driven metaverse announcer in a university campus metaverse prototype, to
conduct user studies for MAUE model. The experimental results have effectively
achieved satisfactory announcer settings that align with the preferences of
most users, encompassing parameters such as video transition rate, repetition
rate, importance threshold value, and image composition.
