  The rapid release of accurate sky localization for gravitational-wave
candidates is crucial for multi-messenger observations. During the third
observing run of Advanced LIGO and Advanced Virgo, automated gravitational-wave
alerts were publicly released within minutes of detection. Subsequent
inspection and analysis resulted in the eventual retraction of a fraction of
the candidates. Updates could be delayed by up to several days, sometimes
issued during or after exhaustive multi-messenger followup campaigns. We
introduce GWSkyNet, a real-time framework to distinguish between astrophysical
events and instrumental artefacts using only publicly available information
from the LIGO-Virgo open public alerts. This framework consists of a
non-sequential convolutional neural network involving sky maps and metadata.
GWSkyNet achieves a prediction accuracy of 93.5% on a testing data set.
