Leveraging Conversation Structure on Social Media to Identify Potentially Influential Users
This addresses the challenge of assessing community reactions on platforms lacking feedback tools, though it appears incremental as it applies existing frameworks to a new domain.
The paper tackled the problem of identifying influential users on social media by modeling conversation structure using Abstract Argumentation Frameworks and machine learning, resulting in evidence that this approach can identify users who produce consistently appreciated content without analyzing the content itself.
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.