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Social networks have a community providing feedback on comments that allowsto identify opinion leaders and users whose positions are unwelcome. Otherplatforms are not backed by such tools. Having a picture of the community'sreactions to a published content is a non trivial problem. In this work wepropose a novel approach using Abstract Argumentation Frameworks and machinelearning to describe interactions between users. Our experiments provideevidence that modelling the flow of a conversation with the primitives of AAFcan support the identification of users who produce consistently appreciatedcontent without modelling such content.

Leveraging Conversation Structure on Social Media to Identify Potentially Influential Users