Name
Title | ||
|---|---|---|
Leveraging Conversation Structure on Social Media to Identify Potentially Influential Users. |
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 communityu0027s 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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Artificial Intelligence | World Wide Web,Conversation,Social network,Social media,Computer science,Argumentation theory,Artificial intelligence,Opinion leadership,Machine learning |
DocType | Volume | Citations |
Journal | abs/1711.10768 | 0 |
PageRank | References | Authors |
0.34 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dario De Nart | 1 | 34 | 7.70 |
| Dante Degl'Innocenti | 2 | 13 | 4.45 |
| Marco Pavan | 3 | 12 | 2.29 |