As blogs have become one of the fastest growing types of Web-based media, bloggers can express their opinions and emotions more freely and easily than before. In the blogspace, many communities have emerged, which include racists and hate groups that are trying to share their ideology, express their views, or recruit new group members. It is important to analyze these cyber communities, defined based on group membership and subscription linkages, in order to monitor for activities that are potentially harmful to society. While Web mining and network analysis techniques have been widely used to analyze the content and structure of the Web sites of hate groups on the Internet, these techniques have not been applied to the study of hate groups in blogs. In this research, we propose a framework to address this problem. The framework consists of four modules, namely blog spider, information extraction, network analysis, and visualization. We applied this framework to identify and analyze a selected set of 28 anti-Blacks hate groups (820 bloggers) on Xanga, one of the most popular blog hosting sites. Our analysis results revealed some interesting demographical and topological characteristics in these groups, and identified at least two large communities on top of the smaller ones. We suggest that the proposed framework can be generalized and applied to blog analysis in other domains.