Online social network is a major media for many types of information communication. Although the primary purpose of social networks is to connect people, they are more and more used in online marketing to connect businesses with customers as well as to connect customers amongst themselves. Brand stories generated by consumers or businesses can be easily and widely spread. As a result, those stories have a huge influence on the marketplace and indirectly affect the brand success. Understanding and modeling how a piece of information is spread on social media and its spreading level are crucial for business managers; not only to understand the information diffusion, but also for them to better control it. In this paper, we aim at developing models in order to predict the spread of brand stories on social networks, both in term of spreadability and spreading level. We applied several machine learning algorithms using three categories of features based on user-profile, temporal, and content of tweets. Experimental results on three tweet collections about brand stories reveal that our model significantly improves the prediction accuracy by about 4% compared to the related work.

在线社交网络是多种信息交流的主要媒体。尽管社交网络的主要目的是连接人们，但它们越来越多地用于在线营销中，以将企业与客户联系起来以及将客户彼此联系起来。消费者或企业产生的品牌故事可以轻松广泛传播。结果，这些故事对市场产生了巨大的影响，并间接影响了品牌的成功。了解和建模一条信息如何在社交媒体上传播及其传播水平对于业务经理来说至关重要；不仅要了解信息扩散，还要让他们更好地控制它。在本文中，我们旨在开发模型以预测品牌故事在社交网络上的传播，在铺展性和铺展水平方面。我们使用基于用户个人资料、时间和推文内容的三类特征应用了几种机器学习算法。三个关于品牌故事的推文集的实验结果表明，与相关工作相比，我们的模型显着提高了预测准确率约 4%。