Search

A topic that involves communities with different competing viewpoints or stances is usually reported by a large number of documents. Knowing the association between the persons mentioned in the documents can help readers construct the background knowledge of the topic and comprehend the numerous topic documents more easily. In this paper, we investigate the stance community identification problem where the goal is to cluster important persons mentioned in a set of topic documents into stance-coherent communities. We propose a stance community identification method called SCIFNET, which constructs a friendship network of topic persons from topic documents automatically. Stance community expansion and stance community refinement techniques are designed to identify stance-coherent communities of topic persons in the friendship network and to detect persons who are stance-irrelevant about the topic. The results of experiments based on real-world datasets demonstrate the effectiveness of SCIFNET and show that it outperforms many well-known community detection approaches and clustering algorithms. [ABSTRACT FROM AUTHOR]

Online group buying involves risks and uncertainties for buyers. Because buyers may not benefit from a failed group buying auction, they usually consider several factors before deciding to join an auction. Although the factors that affect buyers’ decisions have been investigated in the literature, few studies have attempted to utilize them to predict an auction’s success. In this paper, we propose an effective method for predicting the success of a group buying auction. We model success prediction as a classification problem, and utilize five dimensions and thirteen features derived from previous research to predict the success of group buying auctions. Experiments based on a real-world group buying platform demonstrate the efficacy of the proposed method. Moreover, the method outperforms a social propagation model in terms of the prediction precision rate, recall rate, and F1 score. [ABSTRACT FROM AUTHOR]

Abstract: Business indices and indicators are used to monitor the regime shifts of business cycles. Generally, the indices and indicators are comprised of various economic variables that are compiled by different government departments. Compiling the variables involves a great deal of data processing, which delays the monitoring of business cycles. In this paper, we propose a novel business cycle surveillance system that utilizes the query logs of search engines for business cycle modeling. The system employs an effective feature selection technique to identify query terms that are representative of business cycles. The selected terms and the frequency count of queries associated with the terms are then integrated to classify the status of business cycles. We use data discretization techniques to reduce the sparseness of query frequencies. Experimental results based on a five-year dataset show that the proposed system can classify the status of business cycles accurately, and the selected query terms reveal interesting human behavior patterns in different business cycles. Unlike economic variables, query logs are readily available through online Web services, so our system can provide business cycle information in a timely manner. [Copyright &y& Elsevier]