  In this work, we provide an in-depth analysis of collusive entities on
YouTube fostered by various blackmarket services. Following this, we propose
models to detect three types of collusive YouTube entities - videos seeking
collusive likes, channels seeking collusive subscriptions, and videos seeking
collusive comments. The third type of entity is associated with temporal
information. To detect videos and channels for collusive likes and
subscriptions respectively, we utilize one-class classifiers trained on our
curated collusive entities and a set of novel features. The SVM-based model
shows significant performance with a true positive rate of 0.911 and 0.910 for
detecting collusive videos and collusive channels respectively. To detect
videos seeking collusive comments, we propose CollATe, a novel end-to-end
neural architecture that leverages time-series information of posted comments
along with static metadata of videos. CollATe is composed of three components -
metadata feature extractor (which derives metadata-based features from videos),
anomaly feature extractor (which utilizes the comment time-series data to
detect sudden changes in the commenting activity), and comment feature
extractor (which utilizes the text of the comments posted during collusion and
computes a similarity score between the comments). Extensive experiments show
the effectiveness of CollATe (with a true positive rate of 0.905) over the
baselines.
