Analytical Methods for Huge Capacity of Consumer Generated Contents using Inner-Article Impression and Inter-Article Network

用户生成的内容，即博客和推特，是由许多作者撰写的，最近在社会信息共享中所占的比例越来越大。在这项研究中，我们发展了几种分析方法，既利用了每一篇文章的印象，也利用了文章之间的关系。并提出了新型的导航信息检索方法。我们使用社区问答网站的档案数据进行了评估，结果表明我们提出的方法是有效的。

User generated contents i.e. Blogs and Twitters are written by many authors and are recently growing as a bigger part of social information sharing. In this research, we have developed several analyzing methods using both an impression of each articles and a relationship among the articles. And also we have proposed new type of navigational information retrieval methods. Our evaluation using the archive data of community question and answering site shows effectiveness of our proposed ones.