  How can web services that depend on user generated content discern fake
social engagement activities by spammers from legitimate ones? In this paper,
we focus on the social site of YouTube and the problem of identifying bad
actors posting inorganic contents and inflating the count of social engagement
metrics. We propose an effective method, Leas (Local Expansion at Scale), and
show how the fake engagement activities on YouTube can be tracked over time by
analyzing the temporal graph based on the engagement behavior pattern between
users and YouTube videos. With the domain knowledge of spammer seeds, we
formulate and tackle the problem in a semi-supervised manner --- with the
objective of searching for individuals that have similar pattern of behavior as
the known seeds --- based on a graph diffusion process via local spectral
subspace. We offer a fast, scalable MapReduce deployment adapted from the
localized spectral clustering algorithm. We demonstrate the effectiveness of
our deployment at Google by achieving an manual review accuracy of 98% on
YouTube Comments graph in practice. Comparing with the state-of-the-art
algorithm CopyCatch, Leas achieves 10 times faster running time. Leas is
actively in use at Google, searching for daily deceptive practices on YouTube's
engagement graph spanning over a billion users.
