Online communities generate major economic value and currently form pivotal parts of corporate expertise management, marketing and product support. Exploiting the full value of these communities, as well as maintaining their growth, popularity and adoption requires the creation of analysis methods that can help community owners and managers to monitor and understand the dynamics of their communities. Of particular importance is understanding the behaviour that users exhibit in online communities, since changes in these behaviours could affect the utility of the community. In this document we summarise the work produced within the ROBUST project to represent the behaviour of a community of users using numeric features and to discover the types of behaviour, or roles, that users assume within online communities. We explain the process of extracting behavioural features, the combined approach of clustering and role label derivation through which we identify roles that are present within a given online community platform (as an example using IBM Connections), and the rule-based methodology that we have implemented to infer the roles that users assume over time.