  Time-domain astrophysics continues to grow rapidly, with the inception of new
surveys drastically increasing data volumes. Democratised, distributed
approaches to training sets for machine learning classifiers are crucial to
make the most of this torrent of discovery -- with citizen science approaches
proving effective at meeting these requirements. In this paper, we describe the
creation of and the initial results from the $\textit{Kilonova Seekers}$
citizen science project, built to find transient phenomena from the GOTO
telescopes in near real-time. $\textit{Kilonova Seekers}$ launched in July 2023
and received over 600,000 classifications from approximately 2,000 volunteers
over the course of the LIGO-Virgo-KAGRA O4a observing run. During this time,
the project has yielded 20 discoveries, generated a `gold-standard' training
set of 17,682 detections for augmenting deep-learned classifiers, and measured
the performance and biases of Zooniverse volunteers on real-bogus
classification. This project will continue throughout the lifetime of GOTO,
pushing candidates at ever-greater cadence, and directly facilitate the
next-generation classification algorithms currently in development.
