Urban area function zoning based on user relationships in location-based social networks

This is the final version. Available on open access from IEEE via the DOI in this recordWith advanced development of Internet communication and ubiquitous computing, Social Networks are providing an important information channel for smart city construction. Therefore, analyzing Location-based Social Network is a very valuable work in achieving reasonable urban zoning. In Social Networks, a main purpose of prestige assessment is to extract influential users who are regarded as the key nodes for community detection from Onine Social Networks (OSNs). However, social relationships of users are rarely used to evaluate the popularity of physical locations and zone physical locations. In order to achieve urban area function zoning by evaluating the prestige of geographic regions based on user relationships in Location based Social Networks (LBSNs), this paper proposes a Prestige Density-Based Spatial Clustering of Applications with Noise algorithm (P-DBSCAN) by improving the existing DBSCAN algorithm. Specifically, the algorithm first calculates the centrality of users in the social network, and then converts the centrality of users into the location-centrality through the users' check-in data. After the centrality of each location is obtained, the discrete locations are clustered according to four constraints of the given radius. After clustering, the result of urban area function zoning can be achieved. Extensive experiments are conducted for demonstrating the effectiveness of our proposed algorithm in this paper. In addition, the visualization results reveal the correctness of our proposed approach.National Natural Science Foundation of ChinaEuropean Union Horizon 2020Natural Science Basic Research Plan in Shaanxi Province of ChinaFund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi ProvinceMinistry of Science and ICT (MSIT), South KoreaNational Research Foundation of Kore

Sense and Sensibility: What are Customers Looking for in Online Product Reviews? An Abstract

Online product reviews have become the single, largest depository of supplementary information that customers use in their product search, evaluation, and purchase process. The innate value of online reviews lays in the valuable information they provide to prospective customers in their decision making. This has inspired researchers to identify the characteristics of helpful reviews. Extent research suggests that helpful reviews are often of certain numeric features, such as lengthy details and unequivocal rating (Karimi and Wang 2017; Mudambi and Schuff 2010). However, these numeric features do not reveal the nature of the review content. Recognizing the importance of review content, recent studies gear toward examining review content, with a focus on sentiment, using text analysis techniques (Cao et al. 2011; Salehan and Kim 2016). Apart from sentiment, the impact of other content characteristics of online reviews is largely unknown. This research explores online review content by decomposing and comparing three fundamental information components that a review may contain: sensory information (i.e., reviewer’s observation), cognitive information (i.e., thoughts/analysis), and affective information (i.e., emotions). These components are directly associated with three fundamental psychological processes (observation, thinking, and emotion) that people experience to interact with and make sense of the world. When writing a review, reviewers tangle various types of information together to construct narratives and express opinions, creating a complex review content. Readers, on the other side, retrieve and evaluate the information of all types to form an opinion on the quality and helpfulness of the review. Distinguishing these different information components and analyzing their direct and combined effects can significantly enhance our knowledge of consumer’s information needs and online search behavior. This research performs text analysis to capture the three types of information in online product reviews, analyze their patterns and effects on perceived information value. Results from analyzing a sample of 56,752 reviews from Amazon.com indicate that sensory information in online review content has a significantly positive effect on online review helpfulness; whereas, this effect is insignificant for cognitive information, and significantly negative for affective information. This indicates that review readers highly value reviewer’s observations and their expression of sensory experience, are indifferent toward reviewer’s thoughts and analysis, and dislike expression of emotions in review content. This pattern is more salient in reviews of search goods than those of experience goods