Learning for Detecting Norm Violation in Online Communities
This work addresses norm interpretation issues in online communities like Wikipedia, but it is incremental as it applies an existing method to a specific domain.
The paper tackles the problem of detecting norm violations in online communities by proposing a machine learning framework using Logistic Model Trees, evaluated on Wikipedia edit data with a focus on vandalism detection.
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.