Research On The Number Of Individuals Affected By Weibo Containing Negative Word Of Mouth

As a new type of customer satisfaction in the age of the Internet, e-Word of Mouth is a reflection that users express the use experience, intuition and emotions through Internet. With the development of the Internet, the quality information becomes more open, so the propagation of e-word of mouth for product shows new features. The current study focused largely on positive word of mouth in the area of marketing. Even the study on the propagation of word of mouth is more focused on optimizing propagation mechanism. As the self-developed media channels increasingly developed, how many people can the word of mouth especially negative word of mouth affect is a new problem. This paper researched the influence area of product’s negative e-word of mouth based on background mentioned above.A systematic review of word of mouth and its current research condition are illustrated at the beginning of the thesis, and then the development and characteristics of complex network theory are described. On this basis, the classical model of complex networks and information dissemination dynamics model are introduced. According to the propagation characteristics of negative word of mouth regarding product quality, the thesis summaries Weibo information dissemination model (revised) as basic theoretical support for the study of the propagation characteristics of a single micro-blog. Then using a software to track how a single micro-blog spread, it uncovered the propagation features of negative e-word of mouth appeared in the process of spreading in Sina Weibo, such as the propagation life cycle, core node in spread nodes and propagation networks showing a mesh topology.After analyzing the data of a single micro-blog, the thesis selects the micro-blogs related to negative e-word of mouth of product quality in the period from September to December2013, and then does screening and statistics. It shows Sina Weibo users’habits through the consistency test, and summaries the number of retweets and comments of samples by industry and user types respectively. It gets average retweets number and average comments number regarding negative word of mouth from all sectors of product. Analysis of sample data can tap out the distribution patterns of comments and retweets numbers distinguished by various industries or the various types of users comments and retweets numbers, whereby this article does simulation based on distribution patterns of the sample data. Using crystal ball to design simulation, the thesis predicts the number that the micro-blogs related to different industries or published by different types of users containing negative word of mouth can directly influence and potential impact.The research on the number of being direct influenced people shows that the Weibo containing negative word of mouth of product can directly affect3.5individuals, the results will vary between the different industries but not the user types. While, the research on the number of being potential influenced people shows that the Weibo containing negative word of mouth of product can potentially affect20,000individuals. the number is much larger than the negative spread in traditional environment may affect. The number of potential influence among the industry or different types of users due to the number of their fans differently.Application software, statistical analysis and simulation are the main research methods. In this paper, Sina Weibo platform is used to study the spreading characteristics of product’s negative word of mouth, and how many people the product’s negative word of mouth can affect. The research findings have some groundbreaking, answering how many people will be directly and potentially influenced by the Weibo containing product’s negative word of mouth. However, due to the characteristics of Sina Weibo users, the research result has significant Sina Weibo features, so it cannot be fully extended to all Internet platforms.