Leveraging Conversation Structure on Social Media to Identify Potentially Influential Users

TL;DR

This paper introduces a novel method combining Abstract Argumentation Frameworks and machine learning to analyze social media conversations, aiming to identify influential users based on interaction patterns rather than content.

Contribution

It proposes a new approach leveraging conversation structure and argumentation models to detect influential users without analyzing the actual content.

Findings

Model effectively identifies influential users based on interaction patterns.

Using AAF primitives improves understanding of conversation flow.

Method outperforms content-based approaches in influence detection.

Abstract

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.