Core Users Research Articles

Designing sustainable online support: Examining the effects of design change in 49 online health support communities

Online social support communities can significantly improve health outcomes for individuals living with disease. Although they are well studied in the literature, little research examines how sociotechnical design changes influence the sustainability of support communities for different medical conditions. We compare the impact of a single design change on 49 disease‐specific health support forums hosted on the WebMD platform, a popular online health information service. A statistical analysis showcases changes in posting patterns before and after the design intervention; a subsequent interpretive examination of forum content reveals how the design change affected members' perceived affordances of the platform. Our findings suggest that, despite differences between communities, the design change triggered a common set of cascading effects: it made it difficult for core users to create and maintain relationships, that led them to ultimately leave the site, and, in turn, reduced the activity drawing newcomers to the platform. Using these findings, we argue that the design of sustainable and robust online communities must account for systemic, sociotechnical dynamics.

Experiences of Nurses Working in a Fully Digital Hospital: A Phenomenological Study

With the increasing development and integration of information and communication technology (ICT) into hospitals, there remains a lack of understanding of the impact of these technologies on the hospital's largest core users: nurses. Humber River Hospital (HRH), one of the first hospitals to completely integrate technology across all hospital systems and workflows, has sought to understand how ICTs have transformed the clinical working environment. The aim of the study was to achieve a deeper understanding of the lived experiences of nurses practising in North America's first digital hospital. The methodological approach was informed by van Manen's hermeneutic phenomenological methodology. Data were gathered through in-depth semi-structured interviews with eight nurses at HRH. Thematic analysis was conducted using the van Manen and Colaizzi methods of data analysis. Six thematic categories that formed the nurses' lived experiences of working in a digital environment were identified: safety, time, teamwork, technology failures, patient responses and adapting. Nurses at HRH identified six themes regarding their lived experiences working in a fully digital hospital that provide an insight into nurses' values and cause us to reflect on how we might use this information to further support nursing practice in the fully digital environment. Nurses at HRH seem to have normalized the nursing process within the fully digital environment. Technology appears to be viewed by nurses at HRH within the premise of nursing as an art, allowing patient responses to be acknowledged and incorporated into nursing workflows, and as a science, permitting safe care delivery. Overall, nurses perceived technology as being essential for patient safety and facilitating nursing practice. These findings offer insight into nurses' perception of ICTs, and as technological advancements continue to emerge, these findings will inform education, practice and policy.

In Search for Stability in Crypto-Assets: Are Stablecoins the Solution?

Stablecoins claim to stabilise the value of major currencies in the volatile crypto-asset market. This paper describes the often complex functioning of different types of stablecoins and proposes a taxonomy of stablecoin initiatives. To this end it relies on a novel framework for their classification, based on the key dimensions that matter for crypto-assets, namely: (i) accountability of issuer, (ii) decentralisation of responsibilities, and (iii) what underpins the value of the asset. The analysis of different types of stablecoins shows a trade-off between the novelty of the stabilisation mechanism used in an initiative (from mirroring the traditional electronic money approach to the alleged introduction of an “algorithmic central bank”) and its capacity to maintain a stable market value. While relatively less innovative stablecoins could provide a solution to users seeking a stable store of value, especially if legitimised by the adherence to standards that are typical of payment services, the jury is still out on the potential future role of more innovative stablecoins outside their core user base.

User Roles and Contributions in User Innovation Community

User innovation community is an important source for enterprises to obtain originality and relevant product information. In user innovation communities, users are the creators and participators of content and also the providers of wisdom. Classifying the type of users would help people play a more positive role in the open knowledge communities, and improve the innovation, sharing of knowledge and generation of wisdom more preferably. In order to help enterprises to successfully manage the community’s users, HUAWEI cellphone community is taken as an example to explore different contributions of online users with district behavior characteristics. The user samples are classified by these four indicators to build a matrix classification model of users such as creation number, knowledge degree, in-degree centrality and out-degree centrality with K-means clustering. And it further draws six different types of user roles, including core users, active contributors, passive contributors, social users, information acquiring users and divers. It also provides management strategies for enterprises to classify and manage different types of users when operating user innovation communities.

Resource Allocation for D2D Video Multicast Using Multi-Leader Multi-Follower Stackelberg Game With Personalized Incentives

With the growing demand of mobile multimedia services, device-to-device (D2D) multicast provides an efficacious solution to local content sharing. The radio resource needs to be allocated efficiently to enhance the content sharing via D2D multicast. However, the existing resource allocation schemes for solving D2D multicast usually ignore the attribution problem of mobile users to multiple communities and the problem of social differentiation. Unlike the existing works, this paper performs the D2D video multicast in two steps: community formation and resource allocation. In the process of D2D multicast community formation, the best core users for content distribution in the network are selected in consideration of both physical and social factors, and detect whether there is overlap in the communities to which others belong. Then, we explore mobile users' social features including the historical request file similarity, the QoS request differentiation and the random mobility characteristic to arrange the best attribution schemes targeting at not only ensuring the quality of the video multicast service but also maximizing the content hit ratio. In the resource allocation step, a multi-agent hierarchical learning (MAHL) algorithm with personalized incentives based on multi-leader multi-follower Stackelberg game is proposed to maximize the throughput of the network. Simulations are conducted to reveal that compared with the other three benchmark algorithms, the proposed algorithm can significantly improve the throughput of the network under different scenarios.

UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion.

Privacy intrusion has become a major bottleneck for current trust-aware social sensing, since online social media allows anybody to largely disclose their personal information due to the proliferation of the Internet of Things (IoT). State-of-the-art social sensing still suffers from severe privacy threats since it collects users’ personal data and disclosure behaviors, which could raise user privacy concerns due to data integration for personalization. In this paper, we propose a trust-aware model, called the User and Item Similarity Model with Trust in Diverse Kinds (UISTD), to enhance the personalization of social sensing while reducing users’ privacy concerns. UISTD utilizes user-to-user similarities and item-to-item similarities to generate multiple kinds of personalized items with common tags. UISTD also applies a modified k-means clustering algorithm to select the core users among trust relationships, and the core users’ preferences and disclosure behaviors will be regarded as the predicted disclosure pattern. The experimental results on three real-world data sets demonstrate that target users are more likely to: (1) follow the core users’ interests on diverse kinds of items and disclosure behaviors, thereby outperforming the compared methods; and (2) disclose more information with lower intrusion awareness and privacy concern.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

Black Hole or Brave New World?

Since the late twentieth-century, the popularity and prevalence of digital formats have presented challenges for two groups closely associated with archives: archivists and historians. If archivists cannot fulfill their mandate to preserve our growing digital documentary heritage in the face of rapid technological change, the historians who are one of the core user groups of these records will have very little material with which to reconstruct and analyze the late-twentieth and early-twenty-first centuries. This article explores the benefits and challenges that digitization and digital preservation have brought to archivists, as well as the repercussions of these developments for historians. Although the challenges are significant, there is reason to be optimistic: new archival approaches and technologies are being developed every year, and historians have a tradition of making the most of whatever archivists can preserve.

Quantifying the Effect of Informational Support on Membership Retention in Online Communities through Large-Scale Data Analytics

Participating in online health communities for informational support can benefit patients in various ways. For the online communities to be sustainable and effective for their participants, membership retention and commitment are important. This study explores how informational support requesting and providing by users holding different social roles (core user and periphery user) are related with participants' retention in the community. We first crawled six years of data in the WebMD fibromyalgia forum with around 200,000 posts and 10,000 users. Then a supervised machine learning model is trained and validated to automatically identify the requesting and providing informational support posts exchanged between the members in the community. Lastly, survival analysis was employed to quantify how the informational support requesting and providing by different social roles predicts the member's continued participation in the online community. The results reveal the different influencing mechanism of requesting and providing support from different social roles on the patients' decision to stay in the community. The findings can aid in the design of better support mechanisms to enhance member commitment in online health communities.

Tourism trend and network analysis utilizing big data on social media

Background/Objectives: With evolving trends, tourism is also experiencing more diverse policies and methods of promotion. In particular, with the development and increasing popularity of social media platforms, a new trend is setting in. In line with such changes, the current study sets out to utilize big data on social media platforms to analyze trends in tourism, ways in which tourism elements mutually interact, and analyze patterns, in order to propose tourism promotion strategies and provide related basic data.Methods/Statistical analysis: Analysis on social media platforms were conducted to visually express relationship among nodes and analyze the structure and status of link in quantitative terms. NodeXL is an add-in program to Microsoft Excel; it allows the user to directly collect data from social media platforms to execute matrics, statistics, and visualization. The data was collected from Korea Tourism Organization (KTO)’s Twitter and Facebook accounts. Hashtags (#) on 3,200 posts on the Twitter account were analyzed to compute the tourism trend, and the inter-node interactions and links on the Facebook fan pages were analyzed in terms of network density and centrality to calculate the form and characteristics of social media networks.Findings: By analyzing social media pages that represent promotional efforts for Korean tourism, we were able to find the following results: On the KTO Twitter account, the higher hashtag terms were “eating tour,” and “exciting travel,” which follow the recent tourism trends. However, because of platform restrictions, the Twitter account, rather than engaging in mutual interactions with its users, only tended to deliver information, and was unable to reflect more diverse tourism trends. On Facebook, 348 nodes were actively linked 14.99 times on average, indicating a healthy level of activity. Average degrees of connection was 2.214, which is smaller than average connection distance of small societies, indicating efficient mutual interaction. There were three core user groups, with eleven individuals serving as media nodes, and six users with Eigenvector centrality.Improvements/Applications: Tourism promotion must be executed in line with diverse and latest trends in the field. Because Facebook has a higher level of mutual interaction than Twitter, the account holder can maximize the promotional effects by utilizing individuals that serve as the centrality node. That is to say that promotional strategies that take into account the characteristics of individual social media platform are required.

The impact of auditor’s ethics on the quality of auditing the company's cash flows

The cash flow of the company is a sophisticated complex system, which enhances the quality requirements of the audit. Various factors influence the process of conducting an audit, such as: level of financial market development, regulatory support, trust for audit services, quality of financial information, interest differentiation of parties, qualifications of the auditor, etc. Special attention is required to study ethical aspects of the audit. Therefore, the aim of research is substantiation of the importance of the auditor's ethics and the quality of auditing the company's cash flows to form socially responsible market leaders in the context of asymmetric information. The system approach towards the realization of the research goal allowed to systematize theoretical and methodological principles of the audit of cash flows and develop a conceptual scheme for conducting a cash flow audit of the enterprise taking into consideration the influence of the external environment and the company's development strategy. Based on the comparative analysis of professional auditors’ ethics codes, developed by experts from various countries and international organizations, criteria for the quality of the cash flow audit has been identified as follows: integrity, objectivity, professional competence, confidentiality, professional behavior, etc. It has been also identified that the audit quality depends, to a large extent, on the reliability of information and the possibility of its use by various users. Therefore, the interests of the core users of audit information have been systemized by authors. The mechanism of an ethical audit of the company's cash flow was developed, including policies and procedures, which are inherent in different levels: auditor, enterprise and the environment. The advantages of forming socially responsible leader companies in the asymmetry of information conditions were determined. It is an opportunity for the company to attract investors, skilled personnel and loyal customers and partners. At the same time, the presence of socially responsible auditors and companies can stimulate overall integrity, ethical behavior and sustainable development of society.

SERVQUAL analysis of public bus transport services in Kumasi metropolis, Ghana: Core user perspectives

Encouraging public transport use implies the need to make it attractive to commuters through regular service quality evaluation and modification. Understanding user expectations of public transport are thus valuable, and the evaluations/perceptions are a useful way to gauge the performance of the public transport for core users. Using the SERVQUAL methodology, this study sought to analyse the core public bus transport users’ service quality expectations and perceptions, and its effect on overall satisfaction with public bus transport services in Kumasi. Surveying more than 100 public transport users, the study results are illustrated in the case of Kumasi and are discussed to guide possible bus service quality improvements in the city. Data were analysed using the paired-samples t-test and standard multiple regression techniques. The study findings revealed vast differences between public bus transport service quality expectations and perceptions and a general dissatisfaction with the bus service delivery in the city. The bus service reliability and responsiveness were key to explaining the bus service quality in the city. On the basis of the study findings and international best practices, a number of recommendations were made for the transport operators and the city officials.

Social Attribute Aware Incentive Mechanism for Device-to-Device Video Distribution

To offload and alleviate the heavy base station (BS) traffic load caused by the rapidly growing video services, device-to-device (D2D) communication, as one of the most indispensable technologies of the future cellular networks, can be potentially exploited by mobile users to distribute videos for a BS. In this paper, an effective pricing-based multicast video distribution system and a grid-based clustering method are proposed to support the distribution. Moreover, with the consideration of users’ mobility and social characteristics, we classify them into multicast and core types by studying the user stay probability and familiarity. In particular, core users can cooperate with the BS to distribute videos to the multicast users through intracluster D2D multicast. However, core users cannot selflessly help the BS to distribute videos; instead, they will evaluate their personal benefits before distributing the videos to the multicast users. Further, a Stackelberg game-based pricing mechanism is proposed to inspire the core users to distribute videos. Simulation results demonstrate that the proposed mechanism can not only effectively alleviate the BS traffic load, but also significantly improve the effectiveness and reliability of video transmission.

Extracting Core Users Based on Features of Users and Their Relationships in Recommender Systems

As an effective way to solve information overload, recommender system has drawn attention of scholars from various fields. However, existing works mainly focus on improving the accuracy of recommendation by designing new algorithms, while the different importance of individual users has not been well addressed. In this paper, the authors propose new approaches to identifying core users based on trust relationships and interest similarity between users, and the popular degree, trust influence and resource of individual users. First, the trust degree and interest similarity between all user pairs, as well as the three attributes of individuals are calculated. Second, a global core user set is constructed based on three strategies, which are frequency-based, rank-based, and fusion-sorting-based. Finally, the authors compare their proposed methods with other existing methods from accuracy, novelty, long-tail distribution and user degree distribution. Experiments show the effectiveness of the authors' core user extraction methods.

A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection

With the popularity of social networks such as Facebook and Twitter, more information such as individual’s social connections is considered to make personalized multimedia recommendation, compared to traditional approaches based on the rating matrix. However, the massive data information used for recommendation often contains much personal privacy information. Once the information is obtained by attackers, user’s privacy will be revealed directly or indirectly. This paper proposes a privacy preserving method based on weighted noise injection technique to address the issue of multimedia recommendation in the context of social networks. More specifically, first, we extract core users from entire users. The extracted core users can represent the features of all users adequately. Only the relevant data of core users are then used for rating prediction. Second, we inject different noises to the rating matrix of core users according to different relations between the target user and core users. Third, we use the perturbed matrix to predict the ratings of unused multimedia resources for the target user based on a mixed collaborative filtering approach. By comparing with the traditional noise injection method, the experimental results show that the proposed approach can get better performance of privacy preserving multimedia recommendation.

Mapping Personas: Designing UX Relationships for an Online Coastal Atlas

Abstract Following the emerging importance of networks to digital rhetoric, we theorize user experience (UX) design as a form of networked rhetoric. By networked rhetoric, we mean the act of tracing rhetorical impacts between human and nonhuman actors within networks. While researching emerging trends in this area, we were asked to conduct a usability study of an online coastal atlas. During this research project, we found that the atlas was not usable for many of its core user base, and were able to help the atlas’s development team to rethink their application based on the relationships they wanted users to be able to form with this emerging system. Through our discussion of this research within the purview of networked rhetoric, we provide implications for other digital rhetoricians interested in helping to craft better user experiences for their colleagues, students, and community partners.

Online Placement of Multi-Component Applications in Edge Computing Environments

Mobile edge computing is a new cloud computing paradigm which makes use of small-sized edge-clouds to provide real-time services to users. These mobile edge-clouds (MECs) are located in close proximity to users, thus enabling users to seamlessly access applications running on MECs. Due to the co-existence of the core (centralized) cloud, users, and one or multiple layers of MECs, an important problem is to decide where (on which computational entity) to place different components of an application. This problem, known as the application or workload placement problem, is notoriously hard, and therefore, heuristic algorithms without performance guarantees are generally employed in common practice, which may unknowingly suffer from poor performance as compared to the optimal solution. In this paper, we address the application placement problem and focus on developing algorithms with provable performance bounds. We model the user application as an application graph and the physical computing system as a physical graph, with resource demands/availabilities annotated on these graphs. We first consider the placement of a linear application graph and propose an algorithm for finding its optimal solution. Using this result, we then generalize the formulation and obtain online approximation algorithms with polynomial-logarithmic (poly-log) competitive ratio for tree application graph placement. We jointly consider node and link assignment, and incorporate multiple types of computational resources at nodes.

Pathways to a new way of working: Discoverability

Pathways to a new way of working: Discoverability

Analyzing patterns of contribution to user‐generated tag hierarchies based on core user types

ABSTRACTWidely applied social tagging systems with semantic relations created by users have not been fully explored. This study aims to explore user types and their contribution during construction of the tag hierarchy of Zhihu.com, a social Q&A website in China. We conducted log analysis on 284,018 log records crawled from the website, conducted statistical analysis, social network and temporal analysis. Results show that there are four user types based on user editing preference: concept creator, concept reviser, relation editor and extensive contributor. Results suggest that there are plenty of differences between the four types, including the amount of contribution, the influence on collaboration and the temporal pattern. It was learned that relation editors play a most important role during the construction and extensive contributors contributed most on average. Besides, concept creators' activity level decreased from the beginning and concept revisers have a relatively stable activity pattern. These findings shed light on user contribution patterns during the collaborative construction of tag hierarchies.

Identifying Core Users in Social Resource Recommendation System with K-shell Collapse Sequences

Identifying Core Users in Social Resource Recommendation System with K-shell Collapse Sequences