  In this paper, we focus on normative systems for online communities. The
paper addresses the issue that arises when different community members
interpret these norms in different ways, possibly leading to unexpected
behavior in interactions, usually with norm violations that affect the
individual and community experiences. To address this issue, we propose a
framework capable of detecting norm violations and providing the violator with
information about the features of their action that makes this action violate a
norm. We build our framework using Machine Learning, with Logistic Model Trees
as the classification algorithm. Since norm violations can be highly
contextual, we train our model using data from the Wikipedia online community,
namely data on Wikipedia edits. Our work is then evaluated with the Wikipedia
use case where we focus on the norm that prohibits vandalism in Wikipedia
edits.
