The authors present and evaluate an approach to trend detection in social bookmarking systems using a probabilistic generative model in combination with smoothing techniques. Social bookmarking systems are gaining major interest among researchers in the areas of data mining and Web intelligence, since they provide a large amount of user-generated annotations and reflect the interest of millions of people. Based on a vast corpus of approximately 150 million bookmarks found at del. icio.us, the authors analyze bookmarking and tagging patterns and discuss evidence that social bookmarking systems are vulnerable to spamming. They present a method to limit the impact of spam on a trend detector and provide conclusions as well as directions for future research.
A Probabilistic Generative Model for Mining Cybercriminal Network from Online Social Media: A Review