Erzhong Zhou

Extracting news blog hot topics based on the W2T Methodology

Although topic detection and tracking techniques have made great progress, most of the researchers seldom pay more attention to the following two aspects. First, the construction of a topic model does not take the characteristics of different topics into consideration. Second, the factors that determine the formation and development of hot topics are not further analyzed. In order to correctly extract news blog hot topics, the paper views the above problems in a new perspective based on the W2T (Wisdom Web of Things) methodology, in which the characteristics of blog users, context of topic propagation and information granularity are investigated in a unified way. The motivations and features of blog users are first analyzed to understand the characteristics of news blog topics. Then the context of topic propagation is decomposed into the blog community, topic network and opinion network, respectively. Some important factors such as the user behavior pattern, opinion leader and network opinion are identified to track the development trends of news blog topics. Moreover, a blog hot topic detection algorithm is proposed, in which news blog hot topics are identified by measuring the duration, topic novelty, attention degree of users and topic growth. Experimental results show that the proposed method is feasible and effective. These results are also useful for further studying the formation mechanism of opinion leaders in blogspace.

Research interests: their dynamics, structures and applications in unifying search and reasoning

Most scientific publication information, which may reflects scientists’ research interests, is publicly available on the Web. Understanding the characteristics of research interests from previous publications may help to provide better services for scientists in the Web age. In this paper, we introduce some parameters to track the evolution process of research interests, we analyze their structural and dynamic characteristics. According to the observed characteristics of research interests, under the framework of unifying search and reasoning (ReaSearch), we propose interests-based unification of search and reasoning (I-ReaSearch). Under the proposed I-ReaSearch method, we illustrate how research interests can be used to improve literature search on the Web. According to the relationship between an author’s own interests and his/her co-authors interests, social group interests are also used to refine the literature search process. Evaluation from both the user satisfaction and the scalability point of view show that the proposed I-ReaSearch method provides a user centered and practical way to problem solving on the Web. The efforts provide some hints and various methods to support personalized search, and can be considered as a step forward user centric knowledge retrieval on the Web. From the standpoint of the Active Media Technology (AMT) on the Wisdom Web, in this paper, the study on the characteristics of research interests is based on complex networks and human dynamics, which can be considered as an effort towards utilizing information physics to discover and explain the phenomena related to research interests of scientists. The application of research interests aims at providing scientific researchers best means and best ends in an active way for literature search on the Web.

Hot topic detection in professional blogs

Topics in professional blogs mainly refer to specific techniques. Today, professional blog websites have been important information sources. However, information overload and the uncertainty of topic hotness evaluation have been obstacles for hot topic detection. The paper proposes a method of detecting hot topics in professional blogs. The proposed method is based on the characteristics of the professional blogs and mainly analyzes candidate topics that are likely to be hot. First, a word network based on high frequency keywords and co-occurrences of the keywords is constructed, and then the candidate topics are extracted by analyzing the structure of the word network. The opinion networks with respect to the topics in different time intervals are subsequently constructed for opinion analysis. Finally, hot topics are identified by computing the user participation degree, opinion communication degree, and timeliness of the candidate topics. Experimental results show the proposed method is feasible and reasonable.

Research Interests: Their Dynamics, Structures and Applications in Web Search Refinement

For most scientists, their research interests are dynamically changing all the time. Through an analysis of research interests, we find that all the changes are with some characteristics. Plus, the research interests in the dynamic changing process are not isolated, instead, they are interconnected as a whole to form a holistic structure. We introduce some measurement parameters to track and detect the evolution process, we analyze the structural and dynamic characteristics of research interests through statistical analysis, and we also investigate on how they affect each other. As a possible application, we use observed characteristics of research interests to refine literature search on the Web, which shows that diverse user needs can be satisfied using various observations from research interests as constraints for vague queries. Such effort may provide some hints and various methods to support personalized search, and can be considered as a step forward user centric knowledge retrieval on the Web.

Hot Topic Detection in News Blog Based on W2T Methodology

A social event is often unlimitedly amplified and promptly spread in blogspace, and it is valuable to correctly detect blog hot topics for managing the cyberspace. Although hot topic detection techniques have a great improvement, it is more significant o find what determines the life span of a blog topic, because the online consensus brought by the topic unavoidably experiences the real life. The W2T (Wisdom Web of Things) methodology considers the information organization and management from the perspective of Web services, which contributes to a deep understanding of online phenomena such as users’ behaviors and comments in e-commerce platforms and online social networks. This chapter first applies the W2T methodology to analyze the formation and evolution of a blog hot topic, and some influential factors which determine the development of the topic are identified to recognize hot topics. And then, the construction of a blog topic model considers information granularity in order to detect and track the evolution of the topic. Experimental results show that the proposed method for detecting the blog hot topic is feasible and effective.

Hot topic detection in news blogs from the perspective of w2t

News blog hot topics are important for the information recommendation service and marketing. However, information overload and personalized management make the information arrangement more difficult. Moreover, what influences the formation and development of blog hot topics is seldom paid attention to. In order to correctly detect news blog hot topics, the paper first analyzes the development of topics in a new perspective based on W2T (Wisdom Web of Things) methodology. Namely, the characteristics of blog users, context of topic propagation and information granularity are unified to analyze the related problems. Some factors such as the user behavior pattern, network opinion and opinion leader are subsequently identified to be important for the development of topics. Then the topic model based on the view of event reports is constructed. At last, hot topics are identified by the duration, topic novelty, degree of topic growth and degree of user attention. The experimental results show that the proposed method is feasible and effective.

Suitable Route Recommendation Inspired by Cognition

With the increasing popularity of mobile phones, large amounts of real and reliable mobile phone data are being generated every day. These mobile phone data represent the practical travel routes of users and imply the intelligence of them in selecting a suitable route. Usually, an experienced user knows which route is congested in a specified period of time but unblocked in another period of time. Moreover, a route used frequently and recently by a user is usually the suitable one to satisfy the user’s needs. ACT-R (Adaptive Control of Thought-Rational) is a computational cognitive architecture, which provides a good framework to understand the principles and mechanisms of information organization, retrieval and selection in human memory. In this chapter, we employ ACT-R to model the process of selecting a suitable route of users. We propose a cognition-inspired route recommendation method to mine the intelligence of users in selecting a suitable route, evaluate the suitability of the routes, and recommend an ordered list of routes for subscribers. Experiments show that it is effective and feasible to recommend the suitable routes inspired by cognition.

Cognition-inspired route evaluation using mobile phone data

With the increasing popularity of mobile phones, large amounts of real and reliable mobile phone data are being generated every day. These mobile phone data represent the practical travel routes of users and imply the intelligence of them in selecting a suitable route. Usually, an experienced user knows which route is congested in a specified period of time but unblocked in another period of time. Moreover, a route used frequently and recently by a user is usually the suitable one to satisfy the user’s needs. Adaptive control of thought-rational (ACT-R) is a computational cognitive architecture, which provides a good framework to understand the principles and mechanisms of information organization, retrieval and selection in human memory. In this paper, we employ ACT-R to model the process of selecting a suitable route of users. We propose a cognition-inspired route evaluation method to mine the intelligence of users in selecting a suitable route, evaluate the suitability of the routes, and then recommend an ordered list of routes for subscribers. Experiments show that it is effective and feasible to evaluate the suitability of the routes inspired by cognition.

Detecting Stay Areas from a User’s Mobile Phone Data for Urban Computing

Nowadays, mobile phones are often used as an attractive option for large-scale sensing of human behavior, providing a source of real and reliable data for urban computing. As it is known to all, a user’s behavior often happened at some places where the user stayed over a certain time interval for a trip. For understanding a user’s behavior effectively, we need to detect the places where the user stayed over a certain time interval and we call these places stay areas. In this paper, we propose a method for detecting the stay areas from a user’s mobile phone data. The proposed method can tackle the complicated situations that the general method cannot deal with effectively. Through experimental evaluation, the proposed method is shown to deliver excellent performance.