Design And Implementation Of A Relationship Of Trust Microblogging Network Analysis System

March 2006, the founder of blogger.com Williams(Evan Williams) has launched a website-Twitter social network and micro-blog service, Twitter appears to introduce a vision of the world called microblogging new media forms in. Today, microblogging has become an excellent platform for the exchange of emotion between people, which is the representative of Sina Weibo microblogging many excellent, it has hundreds of millions of users, these users in its capacity as a social media platform, according to personality engaged in selective publication and dissemination of their content, along with the exchange of information, each user’s social relationships are also changing. Today’s society, more and more social problems for the public to maintain a higher vigilance things, others gradually lack of trust, which leads to a lack of integrity phenomenon. In contrast, people of trust microblogging is very strong, often a piece of information can cause intense discussions over a wide range, which is able to promptly release information out of one of the reasons. Social entity to trust the microblogging platform on migration has been rapid development. This trust is mainly due to [1]: on the one hand, a lot of messages are distributed via a real-name authentication of "celebrity", reliable sources of information; on the other hand, users of information is generally illustrated, well-founded, so that also increases other users acceptance of the authenticity of the information; and on the one hand, monitoring efforts microblogging mechanism gradually improve. Based on these two aspects, we have developed a relationship of trust microblogging network analysis system.The system is mainly to achieve a large number of micro-blog user information data and information and data processing, to establish a network of relationships between the user and the user interaction data to improve network diagram. Further, to achieve the analysis of the relationship between the number of network topologies, including an analysis of a user’s Top10 close friend, as well as in the central figure in the network. Word processing on the micro-blog data, users can get the network to discuss a lot about some of the topics. In addition, the user’s interest to fully tap the label information, according to the interest clustering, divide to form different communities, get the interest groups in the network.At last, we developed the final form of the whole system by testing, which is able to run better and feedback to users in time through the form of web pages.