Browse

You are looking at 1 - 10 of 28 items for :

  • Information Management x
  • Business and Economics x
Clear All
Open access

Lu An, Xingyue Yi, Yuxin Han and Gang Li

Abstract

This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

Open access

Yuan Zhang and Hsia-Ching Chang

Abstract

Healthcare communication on Twitter is challenging because the space for a tweet is limited, but the topic is too sophisticated to be concise. Comparing medical-terminology hashtags versus lay-language hashtags, this paper explores the characteristics of healthcare hashtags using an entropy matrix which derived from information theory. In this paper, the entropy matrix comprises of six different components used for constructing a tweet and serves as a framework for the structural analysis with the granularity of tweet composition. These granular components include image(s), text with semantic meanings, hashtag(s), @ username(s), hyperlink, and unused space. The entropy matrix proposed in this paper contributes to a new approach to visualizing the complexity level of hashtag collections. In addition, the calculated entropy could be an indicator of the diversity of a user’s choice across those tweet components. Furthermore, the visualizations (radar graph and scatterplot) illustrate statistical structures and the dynamics of the hashtag collections measured by entropy. The results from this study demonstrate a manifest relationship between tweet composition and the number of being retweeted.

Open access

Minghong Chen, Jingye Qu, Yuan Xu and Jiangping Chen

Abstract

Following an integrated data analytics framework that includes descriptive analysis and multiple automatic content analysis, we examined 265 projects that have been funded by the National Science Foundation (NSF) under the Smart and Connected Health (SCH) program. Our analysis discovered certain characteristics of these projects, including the distribution of the funds over years, the leading organizations in SCH, and the multidisciplinary nature of these projects. We also conducted content analysis on project titles and automatic analysis on the abstracts of the projects, including term frequency/word cloud analysis, clustering analysis, and topic modeling using Biterm method. Our analysis found that five main research areas were explored in these projects: system or platform development, modeling or algorithmic development for various purposes, designing smart health devices, clinical data collection and application, and education and academic activities of SCH. Together we obtained a comparatively fair understanding of these projects and demonstrated how different analytic approaches could complement each other. Future research will focus on the impact of these projects through an analysis of their publications and citations.

Open access

Tingting Jiang, Jiaqi Yang, Cong Yu and Yunxin Sang

Abstract

Mobile devices are gaining popularity among online shoppers whose behavior has been reshaped by the changes in screen size, interface, functionality, and context of use. This study, based on a log file from a cross-border E-commerce platform, conducted a clickstream data analysis to compare desktop and mobile users’ visiting behavior. The original 2,827,449 clickstream records generated over a 4-day period were cleaned and analyzed according to an established analysis framework at the footprint level. Differences are found between desktop and mobile users in the distribution of footprints, core footprints, and footprint depth. As the results show, online shoppers preferred to explore various products on mobile devices and read product details on desktops. The E-commerce mobile application (app) presented higher interactivity than the desktop and mobile websites, thus increasing both user involvement and product visibility. It enabled users to engage in the intended activities more effectively on the corresponding pages. Mobile users were further divided into iOS and Android users whose visiting behaviors were basically similar to each other, though the latter might experience slower response speed.

Open access

John Zhang, Ming Fan, Bin Gu, Vijay Mukherjee, Bin Zhang and J. Leon Zhao

Open access

Liang Hong, Mengqi Luo, Ruixue Wang, Peixin Lu, Wei Lu and Long Lu

Abstract

The concept of Big Data is popular in a variety of domains. The purpose of this review was to summarize the features, applications, analysis approaches, and challenges of Big Data in health care. Big Data in health care has its own features, such as heterogeneity, incompleteness, timeliness and longevity, privacy, and ownership. These features bring a series of challenges for data storage, mining, and sharing to promote health-related research. To deal with these challenges, analysis approaches focusing on Big Data in health care need to be developed and laws and regulations for making use of Big Data in health care need to be enacted. From a patient perspective, application of Big Data analysis could bring about improved treatment and lower costs. In addition to patients, government, hospitals, and research institutions could also benefit from the Big Data in health care.

Open access

Binglu Wang, Yi Bu and Win-bin Huang

Abstract

In the field of scientometrics, the principal purpose for author co-citation analysis (ACA) is to map knowledge domains by quantifying the relationship between co-cited author pairs. However, traditional ACA has been criticized since its input is insufficiently informative by simply counting authors’ co-citation frequencies. To address this issue, this paper introduces a new method that reconstructs the raw co-citation matrices by regarding document unit counts and keywords of references, named as Document- and Keyword-Based Author Co-Citation Analysis (DKACA). Based on the traditional ACA, DKACA counted co-citation pairs by document units instead of authors from the global network perspective. Moreover, by incorporating the information of keywords from cited papers, DKACA captured their semantic similarity between co-cited papers. In the method validation part, we implemented network visualization and MDS measurement to evaluate the effectiveness of DKACA. Results suggest that the proposed DKACA method not only reveals more insights that are previously unknown but also improves the performance and accuracy of knowledge domain mapping, representing a new basis for further studies.

Open access

Jin Zhang, Yanyan Wang, Yuehua Zhao and Xin Cai

Abstract

Research methods play an extremely important role in studies. Statistical methods are fundamental and vital for quantitative research. The authors of this paper investigated the research papers that used statistical methods including parametric inferential statistical methods, nonparametric inferential statistical methods, predictive statistical correlation methods, and predictive statistical regression methods in library and information science and examined the connections and interactions between statistical methods and their application areas including information creation, information selection and control, information organization, information retrieval, information dissemination, and information use. Both an inferential statistical method and graphic clustering visualization method were employed to explore the relationships between statistical methods and application areas and reveal the hidden interaction patterns. As a result, 1821 research papers employing statistical methods were identified among the papers published in six major library and information science journals from 1999 to 2017. The findings showed that application areas affected the types of statistical methods utilized. Studies in information organization and information retrieval tended to employ parametric and nonparametric inferential methods, while correlation and regression methods were applied more in studies in information use, information dissemination, information creation, and information selection and control field. These findings help researchers better understand the statistical method orientation of library and information science studies and assist educators in the field to develop applicable quantitative research methodology courses.

Open access

Xiao Hu

Abstract

Digital libraries have been strategic in preserving and making non-movable cultural heritage information accessible to everyone with network connections. In light of their cultural and historical importance in the ancient “Silk Road,” murals and stone caves in Dunhuang, a remote city in northwest China,have been digitized, and the first batch of digitized visual materials has been made available to the general public through the e-Dunhuang digital library since May 2016. The aim of this study was to systematically evaluate e-Dunhuang from users’ perspectives, through usability testing with nine user tasks in different complexity levels and in-depth interviews with regard to a set of criteria in user experience. The results of quantitative analysis confirmed the overall effectiveness of e-Dunhuang in supporting user task completion and demonstrated significant improvements in several criteria over an earlier panorama collection of Dunhuang caves. The results of qualitative analysis revealed in-depth reasons for why participants felt satisfied with some criteria but had concerns with other criteria. Based on the findings, suggestions are proposed for further improvement in e-Dunhuang. As e-Dunhuang is a representative repository of digitized visual materials of cultural heritage, this study offers insights and empirical findings on user-centered evaluation of cultural heritage digital libraries.

Open access

Alireza Abbasi and Ali Jaafari

Abstract

In this study, we investigated the quantity and impact of worldwide research production in the field of “project management” over the past 38 years. We performed a bibliometric analysis using the Scopus database between 1980 and 2017 to develop an understanding of the evolution of research on “project management.” Using the knowledge of a domain expert in the field of “project management,” we first compiled a set of reliable keywords, which represented the field. Second, we developed a data extraction strategy for searching the phrase “project management” in the title or keyword or abstract of publications by limiting our sources to journals in English. We observed the evolution of this field by analyzing not only the quantity of publications but also their impact (citations) per year and compared their growth trend in four periods. The results of our analysis confirmed that not only the research themes or topics but also the active parties involved in project management research have experienced phonemic changes over time.