  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.
