Maximizing positive opinion influence using an evidential approach

TL;DR

This paper introduces a novel influence maximization model for social networks that leverages belief functions to handle data imperfections and identifies key positive opinion influencers.

Contribution

It presents a new evidential approach for influence maximization that effectively manages data imperfections and targets positive opinion influencers.

Findings

The model effectively detects influential users with positive opinions.

Experimental results demonstrate improved influence spread.

The approach handles data imperfections better than existing methods.

Abstract

In this paper, we propose a new data based model for influence maximization in online social networks. We use the theory of belief functions to overcome the data imperfection problem. Besides, the proposed model searches to detect influencer users that adopt a positive opinion about the product, the idea, etc, to be propagated. Moreover, we present some experiments to show the performance of our model.