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      An Examination of Not-For-Profit Stakeholder Networks for Relationship Management: A Small-Scale Analysis on Social Media

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          Abstract

          Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizations from the U.S., U.K., and Australia, to visually map networks of 579 social media brand pages (represented by nodes), connected by 5,600 edges. This network data is analyzed using a collection of popular graph analysis techniques to assess the differences in the way each of the service organizations manage stakeholder networks. We also compare node meta-information against basic topology measures to emphasize the importance of effectively managing relationships with stakeholders who have large external audiences. Implications and future research directions are also discussed.

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          Resolution limit in community detection

          Detecting community structure is fundamental to clarify the link between structure and function in complex networks and is used for practical applications in many disciplines. A successful method relies on the optimization of a quantity called modularity [Newman and Girvan, Phys. Rev. E 69, 026113 (2004)], which is a quality index of a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total number L of links of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. The probability that a module conceals well-defined substructures is the highest if the number of links internal to the module is of the order of \sqrt{2L} or smaller. We discuss the practical consequences of this result by analyzing partitions obtained through modularity optimization in artificial and real networks.
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            Structural diversity in social contagion.

            The concept of contagion has steadily expanded from its original grounding in epidemic disease to describe a vast array of processes that spread across networks, notably social phenomena such as fads, political opinions, the adoption of new technologies, and financial decisions. Traditional models of social contagion have been based on physical analogies with biological contagion, in which the probability that an individual is affected by the contagion grows monotonically with the size of his or her "contact neighborhood"--the number of affected individuals with whom he or she is in contact. Whereas this contact neighborhood hypothesis has formed the underpinning of essentially all current models, it has been challenging to evaluate it due to the difficulty in obtaining detailed data on individual network neighborhoods during the course of a large-scale contagion process. Here we study this question by analyzing the growth of Facebook, a rare example of a social process with genuinely global adoption. We find that the probability of contagion is tightly controlled by the number of connected components in an individual's contact neighborhood, rather than by the actual size of the neighborhood. Surprisingly, once this "structural diversity" is controlled for, the size of the contact neighborhood is in fact generally a negative predictor of contagion. More broadly, our analysis shows how data at the size and resolution of the Facebook network make possible the identification of subtle structural signals that go undetected at smaller scales yet hold pivotal predictive roles for the outcomes of social processes.
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              Detecting Emotional Contagion in Massive Social Networks

              Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                6 October 2016
                2016
                : 11
                : 10
                : e0163914
                Affiliations
                [1 ]Newcastle Business School, University of Newcastle, Callaghan, Australia
                [2 ]Department of Marketing and Supply Chain Management, School of Business and Economics, Maastricht University, Maastricht, Netherlands and BISS Institute, Maastricht, Netherlands
                [3 ]McIntire School of Commerce, University of Virginia, Charlottesville, Virginia, United States of America
                [4 ]Forethought, Melbourne, Australia
                [5 ]Cambridge Service Alliance, Insitute for Manufacturing, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
                Nankai University, CHINA
                Author notes

                Competing Interests: We have the following interests: Ben Kozary was an employee of Forethought at the time this research was conducted. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

                • Conceptualization: JC BL JW.

                • Data curation: BL JW.

                • Formal analysis: BL JW.

                • Methodology: BL JW B. Kitchens.

                • Project administration: JC JW.

                • Resources: BL JW.

                • Software: BL.

                • Supervision: JC.

                • Validation: BL JW.

                • Visualization: JW BL.

                • Writing – original draft: JW BL JC B. Kitchens MZ B. Kozary.

                • Writing – review & editing: JW BL JC B. Kitchens MZ B. Kozary.

                [¤]

                Current address: Newcastle Business School, University of Newcastle, Level 3, University House, Corner King and Auckland Streets, Newcastle 2300, Australia

                ‡ These authors also contributed equally to this work.

                Article
                PONE-D-16-16957
                10.1371/journal.pone.0163914
                5053609
                27711236
                9f522b9a-646c-4cd3-9fe2-fcac2f7e58b6
                © 2016 Wyllie et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 26 April 2016
                : 17 September 2016
                Page count
                Figures: 7, Tables: 3, Pages: 20
                Funding
                This study did not receive any funding. Ben Kozary was an employee of Forethought at the time this research was conducted. Forethought provided support in the form of salary for author Ben Kozary, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of this author is articulated in the "author contributions" section.
                Categories
                Research Article
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Sciences
                Sociology
                Social Networks
                Computer and Information Sciences
                Network Analysis
                Medicine and Health Sciences
                Health Care
                Health Services Research
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Facebook
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Facebook
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Facebook
                Computer and Information Sciences
                Network Analysis
                Centrality
                Social Sciences
                Sociology
                Communications
                Marketing
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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