Kazuhiro Kazama

Finding authoritative people from the Web

Todays Web is so huge and diverse that it arguably reflects the real world. For this reason, searching the Web is a promising approach to find things in the real world. We present NEXAS, an extension to Web search engines that attempts to find real-world entities relevant to a topic. Its basic idea is to extract proper names from the Web pages retrieved for the topic. A main advantage of this approach is that users can query any topic and learn about relevant real-world entities without dedicated databases for the topic. In particular, we focus on an application for finding authoritative people from the Web. This application is practically important because once personal names are obtained; they can lead users from the Web to managed information stored in digital libraries. To explore effective ways of finding people, we first examine the distribution of Japanese personal names by analyzing about 50 million Japanese Web pages. We observe that personal names appear frequently on the Web, but the distribution is highly influenced by automatically generated texts. To remedy the bias and find widely acknowledged people accurately, we utilize the number of Web servers containing a name instead of the number of Web pages. We show its effectiveness by an experiment covering a wide range of topics. Finally, we demonstrate several examples and suggest possible applications.

Information sharing on Twitter during the 2011 catastrophic earthquake

Such large disasters as earthquakes and hurricanes are very unpredictable. During a disaster, we must collect information to save lives. However, in time disaster, it is difficult to collect information which is useful for ourselves from such traditional mass media as TV and newspapers that contain information for the general public. Social media attract attention for sharing information, especially Twitter, which is a hugely popular social medium that is now being used during disasters. In this paper, we focus on the information sharing behaviors on Twitter during disasters. We collected data before and during the Great East Japan Earthquake and arrived at the following conclusions: Many users with little experience with such specific functions as reply and retweet did not continuously use them after the disaster. Retweets were well used to share information on Twitter. Retweets were used not only for sharing the information provided by general users but used for relaying the information from the mass media. We conclude that social media users changed their behavior to widely diffuse important information and decreased non-emergency tweets to avoid interrupting critical information.

Extracting Communities from Complex Networks by the k-Dense Method

To understand the structural and functional properties of large-scale complex networks, it is crucial to efficiently extract a set of cohesive subnetworks as communities. There have been proposed several such community extraction methods in the literature, including the classical k-core decomposition method and, more recently, the k-clique based community extraction method. The k-core method, although computationally efficient, is often not powerful enough for uncovering a detailed community structure and it produces only coarse-grained and loosely connected communities. The k-clique method, on the other hand, can extract fine-grained and tightly connected communities but requires a substantial amount of computational load for large-scale complex networks. In this paper, we present a new notion of a subnetwork called k-dense, and propose an efficient algorithm for extracting k-dense communities. We applied our method to the three different types of networks assembled from real data, namely, from blog trackbacks, word associations and Wikipedia references, and demonstrated that the k-dense method could extract communities almost as efficiently as the k-core method, while the qualities of the extracted communities are comparable to those obtained by the k-clique method.

Characteristics of information diffusion in blogs, in relation to information source type

A novel method is presented to analyze the dynamics of social media, i.e., information diffusion properties, for information recommendation and ranking. In social media such as blogs, various information diffuses over time. As a result, a network structure is constructed. In an information diffusion network, each influential information source has an affected subnetwork whose nodes are reachable from it. We define three information diffusion properties of the subnetwork using the numbers of three types of directed two-edge connected subgraphs, which are basic structures in a directed acyclic graph such as an information diffusion network. Each basic structure type is related to information scattering, information gathering, or information transmission. We visualized and analyzed the structure of information diffusion networks extracted for various topics. Furthermore, we characterized the information diffusion properties by using the rank correlation coefficient, precision, and mean reciprocal rank and mean average precision of three types of information sources: official sites, news articles, and consumer generated media pages. We found that the three information diffusion properties have different characteristics and give priority to different types of information sources.

Extracting communities in networks based on functional properties of nodes

We address the problem of extracting the groups of functionally similar nodes from a network. As functional properties of nodes, we focus on hierarchical levels, relative locations and/or roles with respect to the other nodes. For this problem, we propose a novel method for extracting functional communities from a given network. In our experiments using several types of synthetic and real networks, we evaluate the characteristics of functional communities extracted by our proposed method. From our experimental results, we confirmed that our method can extract functional communities, each of which consists of nodes with functionally similar properties, and these communities are substantially different from those obtained by the Newman clustering method.

The possibility of social media analysis for disaster management

Collecting, sharing, and delivering information in disaster situations is crucially important. Mass media such as TV, radio, and newspapers have played important roles in information distribution in past disasters and crises. Recently, social media have received much attention for their use as an information sharing tool. Especially, it is said that people used Twitter to collect and share information in the aftermath of the Great East Japan Earthquake. In academic fields, some researchers have started to propose some methods and systems for disaster management by analyzing social media data. Other people doubt whether social media will actually function effectively for disaster management because of uncertainty and inaccuracies related to rumors and misunderstanding. In this paper, we overview current studies of social media analysis for disaster management and explain some studies in detail to show their possibility and availability. We specifically examine situational awareness, user behavior analysis and information propagation analysis, which are three approaches to social media analysis, to clarify what social media analysis can and cannot do. Additionally, we propose some concepts for social media analysis and show how those concepts help to collaborate with us, researchers in social media analysis fields and other research fields.

Classification Method for Shared Information on Twitter Without Text Data

During a disaster, appropriate information must be collected. For example, victims and survivors require information about shelter locations and dangerous points or advice about protecting themselves. Rescuers need information about the details of volunteer activities and supplies, especially potential shortages. However, collecting such localized information is difficult from such mass media as TV and newspapers because they generally focus on information aimed at the general public. On the other hand, social media can attract more attention than mass media under these circumstances since they can provide such localized information. In this paper, we focus on Twitter, one of the most influential social media, as a source of local information. By assuming that users who retweet the same tweet are interested in the same topic, we can classify tweets that are required by users with similar interests based on retweets. Thus, we propose a novel tweet classification method that focuses on retweets without text mining. We linked tweets based on retweets to make a retweet network that connects similar tweets and extracted clusters that contain similar tweets from the constructed network by our clustering method. We also subjectively verified the validity of our proposed classification method. Our experiment verified that the ratio of the clusters whose tweets are mutually similar in the cluster to all clusters is very high and the similarities in each cluster are obvious. Finally, we calculated the linguistic similarities of the results to clarify our proposed methods features. Our method classified topic-similar tweets, even if they are not linguistically similar.

Functional cluster extraction from large spatial networks

We address a problem of extracting functionally similar regions in urban streets regarded as spatial networks. Such characteristics of regions will play important roles for developing and planning city promotion, travel tours and so on, as well as understanding and improving the usage of urban streets. In order to analyze such functionally similar regions, we propose an acceleration method of the FCE (functional cluster extraction) algorithm equipped with the lazy evaluation and pivot pruning techniques, which enables to efficiently deal with several large-scale networks. In our experiments using urban streets of six cities, we show that our proposed method achieved a reasonably high acceleration performance. Then, we show that functional cluster produced by our method are useful for understanding the properties of areas in a series of visualization results.

The k-Dense Method to Extract Communities from Complex Networks

To understand the structural and functional properties of large-scale complex networks, it is crucial to efficiently extract a set of cohesive subnetworks as communities. There have been proposed several such community extraction methods in the literature, including the classical k-core decomposition method and, more recently, the k-clique based community extraction method. The k-core method, although computationally efficient, is often not powerful enough for uncovering a detailed community structure and it only discovers coarse-grained and loosely connected communities. The k-clique method, on the other hand, can extract fine-grained and tightly connected communities but requires a substantial amount of computational load for large-scale complex networks. In this paper, we present a new notion of a subnetwork called k-dense, and propose an efficient algorithm for extracting k-dense communities. We applied our method to the three different types of networks assembled from real data, namely, from blog trackbacks, word associations and Wikipedia references, and demonstrated that the k-dense method could extract communities almost as efficiently as the k-core method, while the qualities of the extracted communities are comparable to those obtained by the k-clique method.

Multi-agent Information Diffusion Model for Twitter

During the 2011 East Japan Great Earthquake Disaster, many people used social media such as Twitter to get important information for their lives. But, generally, social media also has bad side, that is wrong information diffusion problem. In this paper, we will propose a novel multiagent-based information diffusion model, the Agent-based Information Diffusion Model (AIDM), and evaluate it. Up to now, our previous model is based on the SIR model, which is famous as a diffusion model of infection. The SIR model is represented by the stochastic state transition model for whether to propagate the information, and its transition probability is defined as the same value for all agents. However, peoples thinking or actions are not the same. To make persons character heterogeneously, we adopted two elements in our proposal model: user diversity and multiplexing of information paths. From a comparison evaluation, it is shown that the proposed model basically to reproduce the information diffusion as same as the diffusion of real data.