There is a need to understand how to: spread product information to maximum range, identifying influential users, and analyze how they are intrinsically connected in a social network. In this paper, we collected tweets of Huawei Mate 9 to analyze users’ information behavior such as tweeting, forwarding, and commenting on tweets. We applied independent cascade model to this empirical Twitter diffusion network, and found it is proper to fit to the product information diffusion process. Using its network structure and PageRank measurement, we can identify influential nodes, and interpret the intrinsic connection between these influential nodes. Further, it is significant to consider the node’s background, such as interest, occupation, and country when identifying influential nodes. And it is discussed that the tweet content related to novel technology may attract more participation in ordinary users.

中文翻译：

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产品信息在社交网络中的传播

需要了解如何：将产品信息传播到最大范围，识别有影响力的用户以及分析他们在社交网络中的内在联系。在本文中，我们收集了华为Mate 9的推文，以分析用户的信息行为，如推文，转发和评论。我们将独立的级联模型应用于该经验性Twitter传播网络，发现适合于产品信息传播过程。使用其网络结构和PageRank测量，我们可以识别有影响力的节点，并解释这些有影响力的节点之间的内在联系。此外，意义重大在确定有影响力的节点时，要考虑节点的背景，例如兴趣，职业和国家。并且讨论了与新颖技术有关的推特内容可能吸引更多的普通用户参与。