Exploring the Identification and Effects of “Opinion Leader” under Different Information Release Strategies

Abstract

This paper systematically analyzes the models and processes related to wordof-mouth spreading in social networks. This paper simulates the characteristics and rules of word-of-mouth spreading on social network platforms, adopts network evolution models as well as virus spreading models which can precisely reflect the process of word-of-mouth spreading. By computer simulation, the effect of several kinds of parameters in networks and in wordof-mouth spreading model is analyzed. What has been proved, through parameter analysis, is that the secondary "push" of the key node (opinion leader) in social networks has played a significant role in promoting word-of-mouth spreading. In practical applications, shopkeepers can act appropriately to the situation, which means they put in a second period of advertise appropriately after placing one advertisement at random in order to save costs and increase efficiency. 158Social Networking means, those key nodes can influence the trend of public opinion in information diffusion.With the vigorous development of online marketing, the search algorithm or evaluation method for opinion leaders in the potential customer relationship network has become a research focus in multiple crossover fields including complex network analysis, data mining, and word-of-mouth marketing. For example, Liu Zhiming [1] combined micro-blog opinion leaders with user's influence and user's activity to identify opinion leaders, and analyzed their characteristics; He Li [2] used data mining techniques to obtain information related to microblog user characteristics. After analysis, it revealed the feasibility of personalized marketing among Weibo users; Zhang J et al. [3] analyzed the effect of network structure, different characteristics in the word-of-mouth spreading process, and opinion leaders' status; Li F [4] proposed an evaluation index system based on the analysis of social networks; Java et al. [5] also analyzed the topological structure of different types of social networks, such as web-based and micro-blog network, adopting data mining methods, and finally obtained their spreading characteristics.