  In this work we are concerned with the detection of spam in video sharing
social networks. Specifically, we investigate how much visual content-based
analysis can aid in detecting spam in videos. This is a very challenging task,
because of the high-level semantic concepts involved; of the assorted nature of
social networks, preventing the use of constrained a priori information; and,
what is paramount, of the context dependent nature of spam. Content filtering
for social networks is an increasingly demanded task: due to their popularity,
the number of abuses also tends to increase, annoying the user base and
disrupting their services. We systematically evaluate several approaches for
processing the visual information: using static and dynamic (motionaware)
features, with and without considering the context, and with or without latent
semantic analysis (LSA). Our experiments show that LSA is helpful, but taking
the context into consideration is paramount. The whole scheme shows good
results, showing the feasibility of the concept.

Computer Vision and Pattern Recognition;Multimedia