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 opinion-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 produces 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.

中文翻译：

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病毒式营销的证据性正面意见影响措施

病毒式营销是一种相对较新的营销形式，它利用社交网络来推广品牌，产品等。其背后的想法是在网络上找到一组可以引发大量传播和采用的影响者。在本文中，我们将介绍一种基于证据的病毒式营销影响力最大化模型。此外，我们的方法解决了现实世界中病毒营销的三种基于意见的方案。第一种情况涉及对产品有正面评价的影响者。第二种情况涉及对产品有正面评价的影响者，并对也具有正面评价的用户产生影响。第三种情况涉及具有影响力的用户，他们对产品持肯定态度，并对其他用户对该产品的消极看法产生影响。接下来，我们提出了六个影响力措施，每种情况下两个。我们还使用一种影响最大化模型，即针对每种情况检测到的影响者集合。最后，我们通过对从Twitter收集的生成的数据集和真实世界的数据集进行的一些实验，展示了所提出模型在每种影响力度量下的性能。