The Mining Research Of Weibo Key Users And User Community Network

Since the rise of the domestic micro-Bo, the rapid development of China’s Internet sector, especially social media is changing. Weibo has become a tool applied to the public daily life, study and work. The amount of user data generated huge microblogging platform. Huge information in today’s information superhighway(the Internet) to spread rapidly to every corner, Weibo is quietly changing people’s access to information, completely changed the news means of communication, but also to more grass-roots social integration and participate in social activities revolutionary impact.In China, the rapid rise of Web2.0 era, represented Weibo, August 15, 2014, announcement to show Sina Weibo has over 156 million active users, his user registration volume has exceeded 600 million and every day more than 61.4 million active users reside in Sina platform. According to the rules of the microblogging platform, each user has a microblogging Watchlist, this list is microblogging users receive information to provide a basis. The researchers referred to as information transfer channels formed between the microblogging users in touch with another Weibo user attention, the microblogging platform relationships between all users constitute a huge social network Weibo user.This paper analyzes the properties of the individual characteristics of microblogging users and user groups microblogging, use microblogging open platform API to obtain user data and import My SQL database, the basic user community network and tap new improved algorithm based on the proposed mining method. These studies will public opinion microblogging control, network security, a friend recommended, e-commerce has practical significance. The main content of this paper can be divided into three areas, as follows:First, the paper analyzes the Sina microblogging open platform features. We are set forth microblogging open platform API and web crawlers two commonly used data acquisition mode. Analysis gives the merits of the two data acquisition means. In this paper, multi-user frequency control API call to get Weibo user data. User data do extraction, cleaning and other pre-processing, and finally clean up after storing user data to the My SQL database.Second, the article identifies key users of the microblogging platform design. He explained the meaning of microblogging users feature attributes, and these attributes are classified into two others and the personal factor. Sensitive property use characteristics microblogging users to identify key users found. Defined quantitative formulas Weibo user identification key, gives Weibo user identification key model.Third, the paper virtual community network on the microblogging platform is formed between the user’s tap. Define and introduce the micro-blog user community network diagram to explain the implementation process from mining algorithms, mining processes and evaluation of the results of the three aspects of mining. Mining method proposed hierarchical clustering improved user similarity model of Weibo user community cluster analysis results show that the effect of mining communities has improved.