Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor
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 cot-pus 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 defector and provide conclusions as well as directions for future research.
social bookmarking trend detection collaborative tagging delicious folksonomies spam detection