Product information diffusion in a social network

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.

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