Blogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. Bloggers form their virtual communities of similar interests. Activities happened in Blogosphere affect the external world. One way to understand the development on Blogosphere is to find influential blog sites. There are many non-influential blog sites which form the “the long tail”. Regardless of a blog site being influential or not, there are influential bloggers. Inspired by the high impact of the influentials in a physical community, we study a novel problem of identifying influential bloggers at a blog site. Active bloggers are not necessarily influential. Influential bloggers can impact fellow bloggers in various ways. In this paper, we discuss the challenges of identifying influential bloggers, investigate what constitutes influential bloggers, present a preliminary model attempting to quantify an influential blogger, and pave the way for building a robust model that allows for finding various types of the influentials. To illustrate these issues, we conduct experiments with data from a real-world blog site, evaluate multi-facets of the problem of identifying influential bloggers, and discuss unique challenges. We conclude with interesting findings and future work.

Blogs, regularly updated online journals, allow people to quickly and easily create and share online content. Most bloggers write about their everyday lives and generally have a small audience of regular readers. Readers interact with bloggers by contributing comments in response to specific blog posts. Moreover, readers of blogs are often bloggers themselves and acknowledge their favorite blogs by adding them to their blogrolls or linking to them in their posts. This paper presents a study of bloggers’ online and real life relationships in three blog communities: Kuwait Blogs, Dallas/Fort Worth Blogs, and United Arab Emirates Blogs. Through a comparative analysis of the social network structures created by blogrolls and blog comments, we find different characteristics for different kinds of links. Our online survey of the three communities reveals that few of the blogging interactions reflect close offline relationships, and moreover that many online relationships were formed through blogging. 1.

In recent years, spectral clustering method has gained attentions because of its superior performance compared to other traditional clustering algorithms such as K-means algorithm. The existing spectral clustering algorithms are all off-line algorithms, i.e., they can not incrementally update the clustering result given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as real time monitoring of the evolving communities of websphere or blogsphere. Unlike traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing items. This paper extends the standard spectral clustering to such evolving data by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigenvalue system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by standard spectral clustering, it achieves similar accuracy but with much lower computational cost. Close inspection into the blog content shows that the incremental approach can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs.

There are information needs involving costly decisions that cannot be efficiently satisfied through conventional web search engines. Alternately, community centric search can provide multiple viewpoints to facilitate decision making. We propose to discover and model the temporal dynamics of thematic communities based on mutual awareness, where the awareness arises due to observable blogger actions and the expansion of mutual awareness leads to community formation. Given a query, we construct a directed action graph that is time-dependent, and weighted with respect to the query. We model the process of mutual awareness expansion using a random walk process and extract communities based on the model. We propose an interaction space based representation to quantify community dynamics. Each community is represented as a vector in the interaction space and its evolution is determined by a novel interaction correlation method. We have conducted experiments with a real-world blog dataset and have promising results for detection as well as insightful results for community evolution. 1.

This paper describes a comprehensive study on social networks of weblogs integrated with analysis of users ’ reading behavior. The analyzed data are obtained from a Japanese weblog hosting service, Doblog. Four kinds of social networks are generated and analyzed: citation, comment, trackback, and blogroll networks. In addition, the user log data are used to identify readership relations among bloggers. After analysis of more than 50,000 users for about two years, we reveal some interactions between social relations and readership relations. We first show that bloggers read other weblogs on a regular basis (50 % of weblogs that are read at least three times are read every five times a user logs in). We call this relation a regular reading relation (RR relation). Then, prediction of RR relations is done using features from the four kinds of social networks. Lastly, information diffusion on RR relations is analyzed and characterized. Our findings provide an overview of social relations and reading behavior. The results support those of existing studies of social network analysis on the blogosphere.

Weblogs have become a leading form of self-publication on the web. Personal weblogs are often considered to represent a person, and the links between webogs can naturally be given a social interaction. Against this background, finding a community around a given weblog—i.e., identifying a set of weblogs that forms a natural group together with the starting point, because of content or social reasons—is a very natural task. Traditional methods for community finding methods focus almost exclusively on topology analysis. In this paper we present a novel method for discovering weblog communities that incorporates both topology analysis and content analysis. We evaluate our method in a small-scale user study, analyze the contributions of the various components of our approach, and compare it against a state-of-the-art topologybased community finding algorithm. 1.

Abstract — This paper addresses a relatively new text categorization problem: classifying a political blog as either ‘liberal ’ or ‘conservative’, based on its political leaning. Instead of simply using “Bag of Words ” features (BoW) as in previous work, we have explored subjectivity manifested in blogs and used subjectivity information thus found to help build political leaning classifiers. Specifically, our subjectivity based approach is two fold: 1) we identify subjective sentences that contain at least two strong subjective clues based on the General Inquirer dictionary; 2) from subjective sentences identified, we extract opinion expressions and BoW features to build political leaning classifiers. Experiments with a political blog corpus we built show that by using features from subjective sentences can significantly improve the classification performance. In addition, by extracting opinion expressions from subjective sentences, we are able to reveal opinions that are characteristic of a specific political orientation to some extent.

Abstract. Weblogs are becoming an important part of today’s web. Interactions between bloggers cause in the formation of a large social network in every blogsphere. Analysis of this network gives a lot of information in behavioral aspects of bloggers and blog readers. In this paper we introduce the largest dataset of Persian Weblogs that contains comments. Our contribution is twofold: first, we provide basic analysis on the blogsphere, and second we introduce a simple model for distribution of comments in Persian Blogs. 1

Web graphs have been very useful in the structural and statistical analysis of the web. Various models have been proposed to simulate web graphs that generate degree distri-butions similar to the web. Real world blog networks resemble many properties of web graphs. But the dynamic nature of the blogosphere and the link structure evolving due to blog readership and social interactions is not well expressed by the existing models. In this research we propose a model for a blogger to construct blog graphs. We com-bine the existing preferential attachment and random attachment model to generate blog graphs which are type of scale-free networks. The blogger is modeled using read, write, idle states and finite read memory. The combination of these techniques helps in evolution of time stamped blog-blog and post-post network through citations within the blog-blog network. Other parameters like the growth function and the randomness in reading and writing posts help in the formation of graphs with different structural properties. We empirically show that these simulated blog graph exhibits properties similar to the real world blog networks in their degree distributions, degree correlations and clustering coefficient. We believe that this model will help researchers to evaluate and analyze the properties of the blogosphere and facilitate the testing of new algorithms.

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