Data and User Modelling Section 4 The massive amounts of data available in virtual communities can be used to build sophisticated as well as simple models of human interactions that in turn can be used to build systems, and processes that can enhance understanding of ways to support better interaction in these communities. Modelling approaches in virtual communities are needed to understand more complex and more abstract phenomenon and data structures in more concrete ways. Modelling provides the basis for measurement and to identify key metrics such as community structure, conversion rates from readers to contributors, type and degree of cooperation and interactions social dynamics. Important properties of data models are variables and characteristics of the models. Underlying these characteristics is the ability to operationalise and measure the variables. For variables should be used with consistent models that can in turn inform us about why certain communities are successful while others are not. Section 4 presents 4 chapters dealing with user and data modelling of social behaviour, user traces as well context of interactions in virtual communities. Chapter 15 presents work on modeling the diversity of user behavior in online communities. More specifically the au- thors looked at how users contribute and attend to content, and how they form social links with their peers. The chapter also attempts to illustrate the models being examined and parameter estimation procedure. Chapter 16 presents a need to understand the role of context in knowledge-based systems. The chapter shows the rela- tionships between explanation and context and presents different types of explanations in contextual-graphs formalism. The chapter also presents a discussion on a case study of collaborative answer building. Chapter 17 is focused on description and discussion of an agent-based modelling system. The chapter describes an agent that acquires domain knowledge content from a learning history log database in a learning community and automatically generates motivational messages for the learner. Chapter 18 presents a Bayesian Network techniques for modelling complex social systems. It illustrates the use of this