The Web has changed our way of life and the Web 2.0 has changed our way of perceiving and using the Web. Data analysis is now required in a plethora of applications that aim to enrich the experience of people with the Web. We first discuss data mining for the social Web. We elaborate on social network analysis and focus on community mining, then go over to recommendation engines and personalization. We discuss the challenges that emerged through the shift from the traditional Web to Web 2.0. We then focus on two issues - the need to protect Web applications from manipulation and the need to make them adaptive towards change. We first discuss manipulations/attacks in recommender systems and present counter-measures. We then elaborate on how changes/concept drifts can be dealt with in applications that analyze clickstream data, monitor topics in news and blogs, or monitor communities and their evolution.

This tutorial is aimed at novice researchers that have general background in data mining and are interested in understanding the potential and challenges pertinent to the social Web. The participants should have a basic understanding of recommendation engines, personalization and text modeling for mining (vector space models). They will learn how basic techniques are extended and new techniques are designed for mining the Web, especially the social Web. They will also learn about issues that are still open and require further research - research that the tutorial participants may decide to perform themselves.