Fundamental theories governing the number of fiber intersections in random nonwoven fiber webs were developed based on the planar geometry of fiber midpoints distributed in a two-dimensional Poisson field. First, the statistical expectation and variance for the number of fiber intersections in unit web area were obtained as functions of a fixed number of fibers with equal lengths. The theories were extended to the case of a two-dimensional Poisson field by assuming that the number and locations of the fibers are random. The theories are validated by a newly developed computer simulation method employing the concept of “seeding region” and “counting region.” Unlike all previously published papers, it was shown for the first time that the expectations and variances obtained theoretically matched that from computer simulations almost perfectly, validating both the theories and simulation algorithms developed.
Purpose
In this volatile and increasingly fast-revolving world, it has become crucially important to monitor, measure and manage nation image and its dynamic changes in real time. However, few studies have been conducted on a model to measure the image and/or its changes. The purpose of this paper is to find an economically affordable methodology to measure nation image and its changes online in real time.
Design/methodology/approach
The study took an approach to build dynamic ontology that may reflect to change nation image in real-time. With it, the authors attempted to measure nation image in real time.
Findings
Among many social media, the authors found that Wikipedia is particularly suitable for the purpose of measuring nation image. An ontology of nation image was built from the keywords collected from the pages directly related to the big three exporting countries in East Asia, i.e. Korea, Japan and China. The click views on the pages of the countries in two different language editions of Wikipedia, Vietnamese and Indonesian were counted.
Originality/value
The study confirms the objective: the data from a social media service, Wikipedia, may work very well as an economically affordable real-time supplement to offline nation image indices that are currently used.
Data envelopment analysis (DEA) is known as a useful tool that produces many efficient decision-making units (DMUs). Traditional DEA provides relative efficient scores and reference sets, but does not influence and rank the efficient DMUs. This paper suggests a method that provides influence and ranking information by using PageRank as a centrality of Social Network analysis (SNA) based on reference sets and their lambda values. The social network structure expresses the DMU as a node, reference sets as link, and lambda as connection strengths or weights. This paper, with PageRank, compares the Eigenvector centrality suggested by Liu, et al. in 2009, and shows that PageRank centrality is more accurate.
In this study we investigate whether Facebook fan-page posting types and topics have a significant effect on engagement. More specifically, the media type and content theme of posting on Facebook are examined to see whether or not there was a difference between content topics. In order to achieve this goal, we set hypotheses as follows: (1) the media types of posting have a significant effect on engagement; (2) the topics and sentiment polarity of posting have a significant effect on engagement. We tested these hypotheses using research procedures as follows: (1) collection and preprocessing of social-media data, including posting types, comments, and reactions on Facebook fan pages, (2) topic modeling of fan-page postings using R and SAS, (3) testing hypotheses using a negative binomial regression model, and (4) implications and insights for social-media marketing. Topic modeling applying to textual data and sentiment analysis were conducted. After that, in order to find the factors to affect the number of Facebook fan-page engagements, the negative binomial regression model including post type, topic, sentiment, reactions of “love,” “haha,” and their interaction as exploratory variables was considered. Finally, the results show that post type is the most influential factor to affect social-media engagement, and content topics, sentiments of posts and comments also have significant effects on it.
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