  Social networks have a community providing feedback on comments that allows
to identify opinion leaders and users whose positions are unwelcome. Other
platforms are not backed by such tools. Having a picture of the community's
reactions to a published content is a non trivial problem. In this work we
propose a novel approach using Abstract Argumentation Frameworks and machine
learning to describe interactions between users. Our experiments provide
evidence that modelling the flow of a conversation with the primitives of AAF
can support the identification of users who produce consistently appreciated
content without modelling such content.
