  Like other social media websites, YouTube is not immune from the attention of
spammers. In particular, evidence can be found of attempts to attract users to
malicious third-party websites. As this type of spam is often associated with
orchestrated campaigns, it has a discernible network signature, based on
networks derived from comments posted by users to videos. In this paper, we
examine examples of different YouTube spam campaigns of this nature, and use a
feature selection process to identify network motifs that are characteristic of
the corresponding campaign strategies. We demonstrate how these discriminating
motifs can be used as part of a network motif profiling process that tracks the
activity of spam user accounts over time, enabling the process to scale to
larger networks.
