Maximizing positive opinion influence using an evidential approach
This work addresses the problem of optimizing positive opinion spread in social networks for marketers or campaigners, but it appears incremental as it builds on existing influence maximization methods with a specific focus on positivity.
The paper tackles influence maximization in online social networks by proposing a data-based model that uses belief functions to address data imperfection and focuses on detecting influencers with positive opinions for propagation, with experiments demonstrating its performance.
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.