Junichi Tatemura

Blog Community Discovery and Evolution Based on Mutual Awareness Expansion

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.

Structural and temporal analysis of the blogosphere through community factorization

The blogosphere has unique structural and temporal properties since blogs are typically used as communication media among human individuals. In this paper, we propose a novel technique that captures the structure and temporal dynamics of blog communities. In our framework, a community is a set of blogs that communicate with each other triggered by some events (such as a news article). The community is represented by its structure and temporal dynamics: a community graph indicates how often one blog communicates with another, and a community intensity indicates the activity level of the community that varies over time. Our method, community factorization, extracts such communities from the blogosphere, where the communication among blogs is observed as a set of subgraphs (i.e., threads of discussion). This community extraction is formulated as a factorization problem in the framework of constrained optimization, in which the objective is to best explain the observed interactions in the blogosphere over time. We further provide a scalable algorithm for computing solutions to the constrained optimization problems. Extensive experimental studies on both synthetic and real blog data demonstrate that our technique is able to discover meaningful communities that are not detectable by traditional methods.

Splog detection using self-similarity analysis on blog temporal dynamics

This paper focuses on spam blog (splog) detection. Blogs are highly popular, new media social communication mechanisms. The presence of splogs degrades blog search results as well as wastes network resources. In our approach we exploit unique blog temporal dynamics to detect splogs.n There are three key ideas in our splog detection framework. We first represent the blog temporal dynamics using self-similarity matrices defined on the histogram intersection similarity measure of the time, content, and link attributes of posts. Second, we show via a novel visualization that the blog temporal characteristics reveal attribute correlation, depending on type of the blog (normal blogs and splogs). Third, we propose the use of temporal structural properties computed from self-similarity matrices across different attributes. In a splog detector, these novel features are combined with content based features. We extract a content based feature vector from different parts of the blog -- URLs, post content, etc. The dimensionality of the feature vector is reduced by Fisher linear discriminant analysis. We have tested an SVM based splog detector using proposed features on real world datasets, with excellent results (90% accuracy).

Eigen-trend: trend analysis in the blogosphere based on singular value decompositions

The blogosphere - the totality of blog-related Web sites - has become a great source of trend analysis in areas such as product survey, customer relationship, and marketing. Existing approaches are based on simple counts, such as the number of entries or the number of links. In this paper, we introduce a novel concept, coined eigen-trend, to represent the temporal trend in a group of blogs with common interests and propose two new techniques for extracting eigen-trends in blogs. First, we propose a trend analysis technique based on the singular value decomposition. Extracted eigen-trends provide new insights into multiple trends on the same keyword. Second, we propose another trend analysis technique based on a higher-order singular value decomposition. This analyzes the blogosphere as a dynamic graph structure and extracts eigen-trends that reflect the structural changes of the blogosphere over time. Experimental studies based on synthetic data sets and a real blog data set show that our new techniques can reveal a lot of interesting trend information and insights in the blogosphere that are not obtainable from traditional count-based methods.

Detecting splogs via temporal dynamics using self-similarity analysis

This article addresses the problem of spam blog (splog) detection using temporal and structural regularity of content, post time and links. Splogs are undesirable blogs meant to attract search engine traffic, used solely for promoting affiliate sites. Blogs represent popular online media, and splogs not only degrade the quality of search engine results, but also waste network resources. The splog detection problem is made difficult due to the lack of stable content descriptors.n We have developed a new technique for detecting splogs, based on the observation that a blog is a dynamic, growing sequence of entries (or posts) rather than a collection of individual pages. In our approach, splogs are recognized by their temporal characteristics and content. There are three key ideas in our splog detection framework. (a) We represent the blog temporal dynamics using self-similarity matrices defined on the histogram intersection similarity measure of the time, content, and link attributes of posts, to investigate the temporal changes of the post sequence. (b) We study the blog temporal characteristics using a visual representation derived from the self-similarity measures. The visual signature reveals correlation between attributes and posts, depending on the type of blogs (normal blogs and splogs). (c) We propose two types of novel temporal features to capture the splog temporal characteristics. In our splog detector, these novel features are combined with content based features. We extract a content based feature vector from blog home pages as well as from different parts of the blog. The dimensionality of the feature vector is reduced by Fisher linear discriminant analysis. We have tested an SVM-based splog detector using proposed features on real world datasets, with appreciable results (90% accuracy).

Virtual reviewers for collaborative exploration of movie reviews

We propose a collaborative exploration system that helps users to explore recommendations from various viewpoints. Given ratings and reviews on movies from reviewers, the system provides “virtual reviewers” that represent particular viewpoints. Each virtual reviewer navigates the user by recommending and characterizing both movies and reviewers according to its viewpoint. We have developed a browsing method with virtual reviewers and visual interfaces.

Splog Detection using Content, Time and Link Structures

This paper focuses on spam blog (splog) detection. Blogs are highly popular, new media social communication mechanisms and splogs corrupt blog search results as well as waste network resources. In our approach we exploit unique blog temporal dynamics to detect splogs. The key idea is that splogs exhibit high temporal regularity in content and post time, as well as consistent linking patterns. Temporal content regularity is detected using a novel autocorrelation of post content. Temporal structural regularity is determined using the entropy of the post time difference distribution, while the link regularity is computed using a HITS based hub score measure. Experiments based on the annotated ground truth on real world dataset show excellent results on splog detection tasks with 90% accuracy.

Dynamic label sampling on fisheye maps for information exploration

For data with large dimensionality, placing labels is critical for users comprehension of a scatterplot or a map of items. We propose a dynamic label sampling technique that, combined with graphical fisheye views, selects appropriate labels out of a large set of items on a map. Labels are sampled to give focus and contextual information according to users panning/zooming and filtering operation. The paper also demonstrates an example of visual exploration with the image browser based on our technique.

Social and content-based information filtering for a Web graphics recommender system

Existing social or content-based approaches to filtering-by-example are difficult to apply to image data. To realize a filtering-by-example system for image data, we propose a new approach to combine social and content-based filtering techniques. A content-based sub-system provides two types of clusters, equivalent items and virtual users, to overcome a disadvantage of social filtering, that is, a shortage of ratings. Since items similar in visual properties are not always similar in user tastes, a social sub-system controls the content-based sub-system with an evaluation function that estimates the validity of content-based clusters according to user ratings. Based on this approach, we have developed an image database, Web Graphics Navigator, that recommends graphics for Web pages according to the users tastes. The database has been open to the public on the World-Wide Web to obtain user ratings. A preliminary observation of the user data shows promising results.

Visualizing document space by force-directed dynamic layout

We propose an interactive document keyword layout technique that enables browsing and manipulation of a collection of documents visually. This layout technique applies a force directed graph drawing algorithm and clusters documents and keywords by reacting to a users interaction dynamically. An example of visual interaction is demonstrated on an experimental system.