Impact of the social networking applications for health information management for patients and physicians

Most social network users hold more than one social network account and utilize them in different ways depending on the digital context. For example, friendly chat on Facebook, professional discussion on LinkedIn, and health information exchange on PatientsLikeMe. Thus many web users need to manage many disparate profiles across many distributed online sources. Maintaining these profiles is cumbersome, time consuming, inefficient, and leads to lost opportunity. In this paper we propose a framework for multiple profile management of online social networks and showcase a demonstrator utilising an open source platform. The result of the research enables a user to create and manage an integrated profile and share/synchronise their profiles with their social networks. A number of use cases were created to capture the functional requirements and describe the interactions between users and the online services. An innovative application of this project is in public health informatics. We utilize the prototype to examine how the framework can benefit patients and physicians. The framework can greatly enhance health information management for patients and more importantly offer a more comprehensive personal health overview of patients to physicians.

Larger or broader : performance implications of size and diversity of the knowledge worker's egocentric network. [更多或更广：知识员工个人中心网络大小和 多样性对绩效的影响]

Management scholars and practitioners emphasize the importance of the size and diversity of a knowledge worker's social network. Constraints on knowledge workers’ time and energy suggest that more is not always better. Further, why and how larger networks contribute to valuable outcomes deserves further understanding. In this study, we offer hypotheses to shed insight on the question of the diminishing returns of large networks and the specific form of network diversity that may contribute to innovative performance among knowledge workers. We tested our hypotheses using data collected from 93 R&D engineers in a Sino-German automobile electronics company located in China. Study findings identified an inflection point, confirming our hypothesis that the size of the knowledge worker's egocentric network has an inverted U-shaped effect on job performance. We further demonstrate that network dispersion richness (the number of cohorts that the focal employee has connections to) rather than network dispersion evenness (equal distribution of ties across the cohorts) has more influence on the knowledge worker's job performance. Additionally, we found that the curvilinear effect of network size is fully mediated by network dispersion richness. Implications for future research on social networks in China and Western contexts are discussed.

Communication and organizational social networks : a simulation model

Recent management research has evidenced the significance of organizational social networks, and communication is believed to impact the interpersonal relationships. However, we have little knowledge on how communication affects organizational social networks. This paper studies the dynamics between organizational communication patterns and the growth of organizational social networks. We propose an organizational social network growth model, and then collect empirical data to test model validity. The simulation results agree well with the empirical data. The results of simulation experiments enrich our knowledge on communication with the findings that organizational management practices that discourage employees from communicating within and across group boundaries have disparate and significant negative effect on the social network’s density, scalar assortativity and discrete assortativity, each of which correlates with the organization’s performance. These findings also suggest concrete measures for management to construct and develop the organizational social network.

Grouping people in social networks using a weighted multi-constraints clustering method

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

Animating and sustaining niche social networks

Within the communicative space online Social Network Sites (SNS) afford, Niche Social Networks Sites (NSNS) have emerged around particular geographic, demographic or topic-based communities to provide what broader SNS do not: specified and targeted content for an engaged and interested community. Drawing on a research project developed at the Queensland University of Technology in conjunction with the Australian Smart Services Cooperative Research Centre that produced an NSNS based around Adventure Travel, this paper outlines the main drivers for community creation and sustainability within NSNS. The paper asks what factors motivate users to join and stay with these sites and what, if any, common patterns can be noted in their formation. It also outlines the main barriers to online participation and content creation in NSNS, and the similarities and differences in SNS and NSNS business models. Having built a community of 100 registered members, the staywild.com.au project was a living laboratory, enabling us to document the steps taken in producing a NSNS and cultivating and retaining active contributors. The paper incorporates observational analysis of user-generated content (UGC) and user profile submissions, statistical analysis of site usage, and findings from a survey of our membership pool in noting areas of success and of failure. In drawing on our project in this way we provide a template for future iterations of NSNS initiation and development across various other social settings: not only niche communities, but also the media and advertising with which they engage and interact. Positioned within the context of online user participation and UGC research, our paper concludes with a discussion of the ways in which the tools afforded by NSNS extend earlier understandings of online ‘communities of interest’. It also outlines the relevance of our research to larger questions about the diversity of the social media ecology.

A tool for internet chatroom surveillance

Internet chatrooms are common means of interaction and communications, and they carry valuable information about formal or ad-hoc formation of groups with diverse objectives. This work presents a fully automated surveillance system for data collection and analysis in Internet chatrooms. The system has two components: First, it has an eavesdropping tool which collects statistics on individual (chatter) and chatroom behavior. This data can be used to profile a chatroom and its chatters. Second, it has a computational discovery algorithm based on Singular Value Decomposition (SVD) to locate hidden communities and communication patterns within a chatroom. The eavesdropping tool is used for fine tuning the SVD-based discovery algorithm which can be deployed in real-time and requires no semantic information processing. The evaluation of the system on real data shows that (i) statistical properties of different chatrooms vary significantly, thus profiling is possible, (ii) SVD-based algorithm has up to 70-80% accuracy to discover groups of chatters.

Privacy by Design : Does it matter for social networks?

Privacy is an important component of freedom and plays a key role in protecting fundamental human rights. It is becoming increasingly difficult to ignore the fact that without appropriate levels of privacy, a person’s rights are diminished. Users want to protect their privacy - particularly in “privacy invasive” areas such as social networks. However, Social Network users seldom know how protect their own privacy through online mechanisms. What is required is an emerging concept that provides users legitimate control over their own personal information, whilst preserving and maintaining the advantages of engaging with online services such as Social Networks. This paper reviews “Privacy by Design (PbD)” and shows how it applies to diverse privacy areas. Such an approach will move towards mitigating many of the privacy issues in online information systems and can be a potential pathway for protecting user’s personal information. The research has posed many questions in need of further investigation for different open source distributed Social Networks. Findings from this research will lead to a novel distributed architecture that provides more transparent and accountable privacy for the users of online information systems.

Recommending people in social networks using data mining

This thesis improves the process of recommending people to people in social networks using new clustering algorithms and ranking methods. The proposed system and methods are evaluated on the data collected from a real life social network. The empirical analysis of this research confirms that the proposed system and methods achieved improvements in the accuracy and efficiency of matching and recommending people, and overcome some of the problems that social matching systems usually suffer.

Community college faculty's teaching social networks and their implications for librarians

Collaboration between faculty and librarians is an important topic of discussion and research among academic librarians. These partnerships between faculty and librarians are vital for enabling students to become lifelong learners through their information literacy education. This research developed an understanding of academic collaborators by analyzing a community college faculty's teaching social networks. A teaching social network, an original term generated in this study, is comprised of communications that influence faculty when they design and deliver their courses. The communication may be formal (e.g., through scholarly journals and professional development activities) and informal (e.g., through personal communication) through their network elements. Examples of the elements of a teaching social network may be department faculty, administration, librarians, professional development, and students. This research asked 'What is the nature of faculty's teaching social networks and what are the implications for librarians?' This study moves forward the existing research on collaboration, information literacy, and social network analysis. It provides both faculty and librarians with added insight into their existing and potential relationships. This research was undertaken using mixed methods. Social network analysis was the quantitative data collection methodology and the interview method was the qualitative technique. For the social network analysis data, a survey was sent to full-time faculty at Las Positas College, a community college, in California. The survey gathered the data and described the teaching social networks for faculty with respect to their teaching methods and content taught. Semi-structured interviews were conducted following the survey with a sub-set of survey respondents to understand why specific elements were included in their teaching social networks and to learn of ways for librarians to become an integral part of the teaching social networks. The majority of the faculty respondents were moderately influenced by the elements of their network except the majority of the potentials were weakly influenced by the elements in their network in their content taught. The elements with the most influence on both teaching methods and content taught were students, department faculty, professional development, and former graduate professors and coursework. The elements with the least influence on both aspects were public or academic librarians, and social media. The most popular roles for the elements were conversations about teaching, sharing ideas, tips for teaching, insights into teaching, suggestions for ways of teaching, and how to engage students. Librarians' weakly influenced faculty in their teaching methods and their content taught. The motivating factors for collaboration with librarians were that students learned how to research, students' research projects improved, faculty saved time by having librarians provide the instruction to students, and faculty built strong working relationships with librarians. The challenges of collaborating with librarians were inadequate teaching techniques used when librarians taught research orientations and lack of time. Ways librarians can be more integral in faculty's teaching social networks included: more workshops for faculty, more proactive interaction with faculty, and more one-on-one training sessions for faculty. Some of the recommendations for the librarians from this study were develop a strong rapport with faculty, librarians should build their services in information literacy from the point of view of the faculty instead of from the librarian perspective, use staff development funding to attend conferences and workshops to improve their teaching, develop more training sessions for faculty, increase marketing efforts of the librarian's instructional services, and seek grant opportunities to increase funding for the library. In addition, librarians and faculty should review the definitions of information literacy and move from a skills based interpretation to a learning process.

Augmenting analytical CRM strategies with social BI

Large communities built around social media on the Internet offer an opportunity to augment analytical customer relationship management (CRM) strategies. The purpose of this paper is to provide direction to advance the conceptual design of business intelligence (BI) systems for implementing CRM strategies. After introducing social CRM and social BI as emerging fields of research, the authors match CRM strategies with a re-engineered conceptual data model of Facebook in order to illustrate the strategic value of these data. Subsequently, the authors design a multi-dimensional data model for social BI and demonstrate its applicability by designing management reports in a retail scenario. Building on the service blueprinting framework, the authors propose a structured research agenda for the emerging field of social BI.

A social matching system : using implicit and explicit information for personalized recommendation in online dating service

Online dating websites enable a specific form of social networking and their efficiency can be increased by supporting proactive recommendations based on participants' preferences with the use of data mining. This research develops two-way recommendation methods for people-to-people recommendation for large online social networks such as online dating networks. This research discovers the characteristics of the online dating networks and utilises these characteristics in developing efficient people-to-people recommendation methods. Methods developed support improved recommendation accuracy, can handle data sparsity that often comes with large data sets and are scalable for handling online networks with a large number of users.

Enterprise social networks : a business model perspective

Enterprise Social Networks continue to be adopted by organisations looking to increase collaboration between employees, customers and industry partners. Offering a varied range of features and functionality, this technology can be distinguished by the underlying business models that providers of this software deploy. This study identifies and describes the different business models through an analysis of leading Enterprise Social Networks: Yammer, Chatter, SharePoint, Connections, Jive, Facebook and Twitter. A key contribution of this research is the identification of consumer and corporate models as extreme approaches. These findings align well with research on the adoption of Enterprise Social Networks that has discussed bottom-up and top-down approaches. Of specific interest are hybrid models that wrap a corporate model within a consumer model and may, therefore, provide synergies on both models. From a broader perspective, this can be seen as the merging of the corporate and consumer markets for IT products and services.

A recurrent network in the lateral amygdala : a mechanism for coincidence detection

Synaptic changes at sensory inputs to the dorsal nucleus of the lateral amygdala (LAd) play a key role in the acquisition and storage of associative fear memory. However, neither the temporal nor spatial architecture of the LAd network response to sensory signals is understood. We developed a method for the elucidation of network behavior. Using this approach, temporally patterned polysynaptic recurrent network responses were found in LAd (intra-LA), both in vitro and in vivo, in response to activation of thalamic sensory afferents. Potentiation of thalamic afferents resulted in a depression of intra-LA synaptic activity, indicating a homeostatic response to changes in synaptic strength within the LAd network. Additionally, the latencies of thalamic afferent triggered recurrent network activity within the LAd overlap with known later occurring cortical afferent latencies. Thus, this recurrent network may facilitate temporal coincidence of sensory afferents within LAd during associative learning.

Social networks in arid Australia : a review of concepts and evidence

Arid systems are markedly different from non-arid systems. This distinctiveness extends to arid-social networks, by which we mean social networks which are influenced by the suite of factors driving arid and semi-arid regions. Neither the process of how aridity interacts with social structure, nor what happens as a result of this interaction, is adequately understood. This paper postulates three relative characteristics which make arid-social networks distinct: that they are tightly bound, are hierarchical in structure and, hence, prone to power abuses, and contain a relatively higher proportion of weak links, making them reactive to crisis. These ideas were modified from workshop discussions during 2006. Although they are neither tested nor presented as strong beliefs, they are based on the anecdotal observations of arid-system scientists with many years of experience. This paper does not test the ideas, but rather examines them in the context of five arid-social network case studies with the aim of hypotheses building. Our cases are networks related to pastoralism, Aboriginal outstations, the ‘Far West Coast Aboriginal Enterprise Network’ and natural resources in both the Lake-Eyre basin and the Murray–Darling catchment. Our cases highlight that (1) social networks do not have clear boundaries, and that how participants perceive their network boundaries may differ from what network data imply, (2) although network structures are important determinants of system behaviour, the role of participants as individuals is still pivotal, (3) and while in certain arid cases weak links are engaged in crisis, the exact structure of all weak links in terms of how they place participants in relation to other communities is what matters.

Identifying the Events That Connect Social Media Users : Charting Follower Accession on Twitter

Twitter is the focus of much research attention, both in traditional academic circles and in commercial market and media research, as analytics give increasing insight into the performance of the platform in areas as diverse as political communication, crisis management, television audiencing and other industries. While methods for tracking Twitter keywords and hashtags have developed apace and are well documented, the make-up of the Twitter user base and its evolution over time have been less understood to date. Recent research efforts have taken advantage of functionality provided by Twitter's Application Programming Interface to develop methodologies to extract information that allows us to understand the growth of Twitter, its geographic spread and the processes by which particular Twitter users have attracted followers. From politicians to sporting teams, and from YouTube personalities to reality television stars, this technique enables us to gain an understanding of what prompts users to follow others on Twitter. This article outlines how we came upon this approach, describes the method we adopted to produce accession graphs and discusses their use in Twitter research. It also addresses the wider ethical implications of social network analytics, particularly in the context of a detailed study of the Twitter user base.