# Evidential positive opinion influence measures for viral marketing

## TL;DR

This paper introduces an influence maximization model based on evidential opinions for viral marketing, addressing three opinion scenarios and proposing six influence measures tested on real and synthetic datasets.

## Contribution

It presents a novel evidential opinion-based influence maximization framework tailored for viral marketing, considering diverse opinion influence scenarios.

## Key findings

- The model effectively identifies influential users in different opinion scenarios.
- Proposed influence measures outperform baseline methods in experiments.
- The approach demonstrates applicability on real-world Twitter data.

## Abstract

The Viral Marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large cascade of propagation and adoptions. In this paper, we will introduce an evidential opinion-based influence maximization model for viral marketing. Besides, our approach tackles three opinions based scenarios for viral marketing in the real world. The first scenario concerns influencers who have a positive opinion about the product. The second scenario deals with influencers who have a positive opinion about the product and produce effects on users who also have a positive opinion. The third scenario involves influence users who have a positive opinion about the product and produce effects on the negative opinion of other users concerning the product in question. Next, we proposed six influence measures, two for each scenario. We also use an influence maximization model that the set of detected influencers for each scenario. Finally, we show the performance of the proposed model with each influence measure through some experiments conducted on a generated dataset and a real world dataset collected from Twitter.